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Hosseini K, Behnoush AH, Khalaji A, Etemadi A, Soleimani H, Pasebani Y, Jenab Y, Masoudkabir F, Tajdini M, Mehrani M, Nanna MG. Machine learning prediction of one-year mortality after percutaneous coronary intervention in acute coronary syndrome patients. Int J Cardiol 2024; 409:132191. [PMID: 38777044 DOI: 10.1016/j.ijcard.2024.132191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 04/01/2024] [Accepted: 05/17/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND Machine learning (ML) models have the potential to accurately predict outcomes and offer novel insights into inter-variable correlations. In this study, we aimed to design ML models for the prediction of 1-year mortality after percutaneous coronary intervention (PCI) in patients with acute coronary syndrome. METHODS This study was performed on 13,682 patients at Tehran Heart Center from 2015 to 2021. Patients were split into 70:30 for testing and training. Four ML models were designed: a traditional Logistic Regression (LR) model, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Ada Boost models. The importance of features was calculated using the RF feature selector and SHAP based on the XGBoost model. The Area Under the Receiver Operating Characteristic Curve (AUC-ROC) for the prediction on the testing dataset was the main measure of the model's performance. RESULTS From a total of 9,073 patients with >1-year follow-up, 340 participants died. Higher age and higher rates of comorbidities were observed in these patients. Body mass index and lipid profile demonstrated a U-shaped correlation with the outcome. Among the models, RF had the best discrimination (AUC 0.866), while the highest sensitivity (80.9%) and specificity (88.3%) were for LR and XGBoost models, respectively. All models had AUCs of >0.8. CONCLUSION ML models can predict 1-year mortality after PCI with high performance. A classic LR statistical approach showed comparable results with other ML models. The individual-level assessment of inter-variable correlations provided new insights into the non-linear contribution of risk factors to post-PCI mortality.
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Affiliation(s)
- Kaveh Hosseini
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Hossein Behnoush
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; School of Medicine, Tehran University of Medical Sciences, Tehran, Iran; Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
| | - Amirmohammad Khalaji
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; School of Medicine, Tehran University of Medical Sciences, Tehran, Iran; Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Etemadi
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamidreza Soleimani
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Yeganeh Pasebani
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Yaser Jenab
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Farzad Masoudkabir
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Masih Tajdini
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mehdi Mehrani
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Michael G Nanna
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT, USA
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Bourazana A, Xanthopoulos A, Briasoulis A, Magouliotis D, Spiliopoulos K, Athanasiou T, Vassilopoulos G, Skoularigis J, Triposkiadis F. Artificial Intelligence in Heart Failure: Friend or Foe? Life (Basel) 2024; 14:145. [PMID: 38276274 PMCID: PMC10817517 DOI: 10.3390/life14010145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/08/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024] Open
Abstract
In recent times, there have been notable changes in cardiovascular medicine, propelled by the swift advancements in artificial intelligence (AI). The present work provides an overview of the current applications and challenges of AI in the field of heart failure. It emphasizes the "garbage in, garbage out" issue, where AI systems can produce inaccurate results with skewed data. The discussion covers issues in heart failure diagnostic algorithms, particularly discrepancies between existing models. Concerns about the reliance on the left ventricular ejection fraction (LVEF) for classification and treatment are highlighted, showcasing differences in current scientific perceptions. This review also delves into challenges in implementing AI, including variable considerations and biases in training data. It underscores the limitations of current AI models in real-world scenarios and the difficulty in interpreting their predictions, contributing to limited physician trust in AI-based models. The overarching suggestion is that AI can be a valuable tool in clinicians' hands for treating heart failure patients, as far as existing medical inaccuracies have been addressed before integrating AI into these frameworks.
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Affiliation(s)
- Angeliki Bourazana
- Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece
| | - Andrew Xanthopoulos
- Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece
| | - Alexandros Briasoulis
- Division of Cardiovascular Medicine, Section of Heart Failure and Transplantation, University of Iowa, Iowa City, IA 52242, USA
| | - Dimitrios Magouliotis
- Department of Cardiothoracic Surgery, University of Thessaly, 41110 Larissa, Greece; (D.M.); (K.S.)
| | - Kyriakos Spiliopoulos
- Department of Cardiothoracic Surgery, University of Thessaly, 41110 Larissa, Greece; (D.M.); (K.S.)
| | - Thanos Athanasiou
- Department of Surgery and Cancer, Imperial College London, St Mary’s Hospital, London W2 1NY, UK
| | - George Vassilopoulos
- Department of Hematology, University Hospital of Larissa, University of Thessaly Medical School, 41110 Larissa, Greece
| | - John Skoularigis
- Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece
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3
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Chen YL, Nguyen PA, Chien CH, Hsu MH, Liou DM, Yang HC. Machine learning-based prediction of medication refill adherence among first-time insulin users with type 2 diabetes. Diabetes Res Clin Pract 2024; 207:111033. [PMID: 38049037 DOI: 10.1016/j.diabres.2023.111033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 09/05/2023] [Accepted: 11/30/2023] [Indexed: 12/06/2023]
Abstract
AIMS The prevalence of Type 2 Diabetes Mellitus (T2DM) is projected to be 7 % in 2030. Despite its need for long-term diabetes care, the adherence rate of injectable medications such as insulin is around 60 %, lower than the acceptable threshold of 80 %. This study aims to create classification models to predict insulin adherence among adult T2DM naïve insulin users. METHODS Clinical data were extracted from Taipei Medical University Clinical Research Database (TMUCRD) from January 1st, 2004 to December 30th, 2020. A patient was regarded as adherent if his/her medication possession ratio (MPR) was at least 80 %. Seven domains of predictors were created, including demographics, baseline medications, baseline comorbidities, baseline laboratory data, healthcare resource utilization, index insulins, and the concomitant non-insulin T2DM medications. We built two Xgboost models for internal and external testing respectively. RESULTS Using a cohort of 4134 patients from Taiwan, our model achieved the Area Under the curve of the Receiver Operating Characteristic (AUROC) of the internal test was 0.782 and the AUROC of the external test was 0.771. the SHAP (SHapley Additive exPlanations) value showed that the number of prescribed medications, the number of outpatient visits, and laboratory data were predictive of future insulin adherence. CONCLUSIONS This is the first study to predict adherence among adult naïve insulin users. The developed model is a potential clinical decision support tool to identify possible non-adherent patients for healthcare providers to design individualized education plans.
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Affiliation(s)
- Ya-Lin Chen
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Phung-Anh Nguyen
- Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei, Taiwan; Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan; Research Center of Health Care Industry Data Science, College of Management, Taipei Medical University, Taipei, Taiwan
| | - Chia-Hui Chien
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology (ICHIT), College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Office of Public Affairs, Taipei Medical University, Taiwan
| | - Min-Huei Hsu
- Office of Data Science, Taipei Medical University, Taipei, Taiwan; Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan
| | - Der-Ming Liou
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Hsuan-Chia Yang
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology (ICHIT), College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
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Masiero M, Spada GE, Sanchini V, Munzone E, Pietrobon R, Teixeira L, Valencia M, Machiavelli A, Fragale E, Pezzolato M, Pravettoni G. A Machine Learning Model to Predict Patients' Adherence Behavior and a Decision Support System for Patients With Metastatic Breast Cancer: Protocol for a Randomized Controlled Trial. JMIR Res Protoc 2023; 12:e48852. [PMID: 38096002 PMCID: PMC10755656 DOI: 10.2196/48852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 09/18/2023] [Accepted: 10/10/2023] [Indexed: 12/31/2023] Open
Abstract
BACKGROUND Adherence to oral anticancer treatments is critical in the disease trajectory of patients with breast cancer. Given the impact of nonadherence on clinical outcomes and the associated economic burden for the health care system, finding ways to increase treatment adherence is particularly relevant. OBJECTIVE The primary end point is to evaluate the effectiveness of a decision support system (DSS) and a machine learning web application in promoting adherence to oral anticancer treatments among patients with metastatic breast cancer. The secondary end point is to collect a set of new physical, psychological, social, behavioral, and quality of life predictive variables that could be used to refine the preliminary version of the machine learning model to predict patients' adherence behavior. METHODS This prospective, randomized controlled study is nested in a large-scale international project named "Enhancing therapy adherence among metastatic breast cancer patients" (Pfizer 65080791), aimed to develop a predictive model of nonadherence and associated DSS and guidelines to foster patients' engagement and therapy adherence. A web-based DSS named TREAT (treatment adherence support) was developed using a patient-driven approach, with 4 sections, that is, Section A: Metastatic Breast Cancer; Section B: Adherence to Cancer Therapies; Section C: Promoting Adherence; and Section D: My Adherence Diary. Moreover, a machine learning-based web application was developed to predict patients' risk factors of adherence to anticancer treatment, specifically pertaining to physical status and comorbid conditions, as well as short and long-term side effects. Overall, 100 patients consecutively admitted at the European Institute of Oncology (IEO) at the Division of Medical Senology will be enrolled; 50 patients with metastatic breast cancer will be exposed to the DSS and machine learning web application for 3 months (experimental group), and 50 patients will not be exposed to the intervention (control group). Each participant will fill a weekly medication diary and a set of standardized self-reports evaluating psychological and quality of life variables (Adherence Attitude Inventory, Beck Depression Inventory-II, Brief Pain Inventory, 13-item Sense of Coherence scale, Brief Italian version of Cancer Behavior Inventory, European Organization for Research and Treatment of Cancer Quality of Life 23-item Breast Cancer-specific Questionnaire, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, 8-item Morisky Medication Adherence Scale, State-Trait Anxiety Inventory forms I and II, Big Five Inventory, and visual analogue scales evaluating risk perception). The 3 assessment time points are T0 (baseline), T1 (1 month), T2 (2 months), and T3 (3 months). This study was approved by the IEO ethics committee (R1786/22-IEO 1907). RESULTS The recruitment process started in May 2023 and is expected to conclude on December 2023. CONCLUSIONS The contribution of machine learning techniques through risk-predictive models integrated into DSS will enable medication adherence by patients with cancer. TRIAL REGISTRATION ClinicalTrials.gov NCT06161181; https://clinicaltrials.gov/study/NCT06161181. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/48852.
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Affiliation(s)
- Marianna Masiero
- Department of Oncology and Hemato-oncology, University of Milan, Milan, Italy
- Applied Research Division for Cognitive and Psychological Science, European Institute of Oncology IRCCS, Milan, Italy
| | - Gea Elena Spada
- Applied Research Division for Cognitive and Psychological Science, European Institute of Oncology IRCCS, Milan, Italy
| | - Virginia Sanchini
- Department of Oncology and Hemato-oncology, University of Milan, Milan, Italy
| | - Elisabetta Munzone
- Division of Medical Senology, European Institute of Oncology IRCCS, Milan, Italy
| | | | | | | | | | - Elisa Fragale
- Applied Research Division for Cognitive and Psychological Science, European Institute of Oncology IRCCS, Milan, Italy
| | - Massimo Pezzolato
- Department of Oncology and Hemato-oncology, University of Milan, Milan, Italy
- Applied Research Division for Cognitive and Psychological Science, European Institute of Oncology IRCCS, Milan, Italy
| | - Gabriella Pravettoni
- Department of Oncology and Hemato-oncology, University of Milan, Milan, Italy
- Applied Research Division for Cognitive and Psychological Science, European Institute of Oncology IRCCS, Milan, Italy
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5
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Demiray O, Gunes ED, Kulak E, Dogan E, Karaketir SG, Cifcili S, Akman M, Sakarya S. Classification of patients with chronic disease by activation level using machine learning methods. Health Care Manag Sci 2023; 26:626-650. [PMID: 37824033 DOI: 10.1007/s10729-023-09653-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Accepted: 09/04/2023] [Indexed: 10/13/2023]
Abstract
Patient Activation Measure (PAM) measures the activation level of patients with chronic conditions and correlates well with patient adherence behavior, health outcomes, and healthcare costs. PAM is increasingly used in practice to identify patients needing more support from the care team. We define PAM levels 1 and 2 as low PAM and investigate the performance of eight machine learning methods (Logistic Regression, Lasso Regression, Ridge Regression, Random Forest, Gradient Boosted Trees, Support Vector Machines, Decision Trees, Neural Networks) to classify patients. Primary data collected from adult patients (n=431) with Diabetes Mellitus (DM) or Hypertension (HT) attending Family Health Centers in Istanbul, Turkey, is used to test the methods. [Formula: see text] of patients in the dataset have a low PAM level. Classification performance with several feature sets was analyzed to understand the relative importance of different types of information and provide insights. The most important features are found as whether the patient performs self-monitoring, smoking and exercise habits, education, and socio-economic status. The best performance was achieved with the Logistic Regression algorithm, with Area Under the Curve (AUC)=0.72 with the best performing feature set. Alternative feature sets with similar prediction performance are also presented. The prediction performance was inferior with an automated feature selection method, supporting the importance of using domain knowledge in machine learning.
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Affiliation(s)
- Onur Demiray
- Department of Computing, Imperial College London, London, SW7 2AZ, UK
| | - Evrim D Gunes
- College of Administrative Sciences and Economics, Koç University, Rumeli Feneri Yolu, Sariyer-Istanbul, Turkey.
| | - Ercan Kulak
- Ministry of Health Caycuma District Health Directorate, Zonguldak, Turkey
| | - Emrah Dogan
- Ministry of Health, Zonguldak Community Health Center, Zonguldak, Turkey
| | | | - Serap Cifcili
- Department of Family Medicine, Marmara University School of Medicine, Istanbul, Turkey
| | - Mehmet Akman
- Department of Family Medicine, Marmara University School of Medicine, Istanbul, Turkey
| | - Sibel Sakarya
- MPH, MHPE, School of Medicine, Department of Public Health, Koç University, Rumeli Feneri Yolu, Sariyer-Istanbul, Turkey
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Adhikari S, Mukhyopadhyay A, Kolzoff S, Li X, Nadel T, Fitchett C, Chunara R, Dodson J, Kronish I, Blecker SB. Cohort profile: a large EHR-based cohort with linked pharmacy refill and neighbourhood social determinants of health data to assess heart failure medication adherence. BMJ Open 2023; 13:e076812. [PMID: 38040431 PMCID: PMC10693878 DOI: 10.1136/bmjopen-2023-076812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 11/06/2023] [Indexed: 12/03/2023] Open
Abstract
PURPOSE Clinic-based or community-based interventions can improve adherence to guideline-directed medication therapies (GDMTs) among patients with heart failure (HF). However, opportunities for such interventions are frequently missed, as providers may be unable to recognise risk patterns for medication non-adherence. Machine learning algorithms can help in identifying patients with high likelihood of non-adherence. While a number of multilevel factors influence adherence, prior models predicting non-adherence have been limited by data availability. We have established an electronic health record (EHR)-based cohort with comprehensive data elements from multiple sources to improve on existing models. We linked EHR data with pharmacy refill data for real-time incorporation of prescription fills and with social determinants data to incorporate neighbourhood factors. PARTICIPANTS Patients seen at a large health system in New York City (NYC), who were >18 years old with diagnosis of HF or reduced ejection fraction (<40%) since 2017, had at least one clinical encounter between 1 April 2021 and 31 October 2022 and active prescriptions for any of the four GDMTs (beta-blocker, ACEi/angiotensin receptor blocker (ARB)/angiotensin receptor neprilysin inhibitor (ARNI), mineralocorticoid receptor antagonist (MRA) and sodium-glucose cotransporter 2 inhibitor (SGLT2i)) during the study period. Patients with non-geocodable address or outside the continental USA were excluded. FINDINGS TO DATE Among 39 963 patients in the cohort, the average age was 73±14 years old, 44% were female and 48% were current/former smokers. The common comorbid conditions were hypertension (77%), cardiac arrhythmias (56%), obesity (33%) and valvular disease (33%). During the study period, 33 606 (84%) patients had an active prescription of beta blocker, 32 626 (82%) had ACEi/ARB/ARNI, 11 611 (29%) MRA and 7472 (19%) SGLT2i. Ninety-nine per cent were from urban metropolitan areas. FUTURE PLANS We will use the established cohort to develop a machine learning model to predict medication adherence, and to support ancillary studies assessing associates of adherence. For external validation, we will include data from an additional hospital system in NYC.
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Affiliation(s)
- Samrachana Adhikari
- New York University Grossman School of Medicine, New York City, New York, USA
| | | | | | - Xiyue Li
- New York University Grossman School of Medicine, New York City, New York, USA
| | - Talia Nadel
- New York University Grossman School of Medicine, New York City, New York, USA
| | - Cassidy Fitchett
- New York University Grossman School of Medicine, New York City, New York, USA
| | - Rumi Chunara
- New York University, New York City, New York, USA
| | - John Dodson
- New York University Grossman School of Medicine, New York City, New York, USA
| | - Ian Kronish
- Center Behavioral Cardiovascular Health, Columbia University Medical Center, New York City, New York, USA
| | - Saul B Blecker
- New York University Grossman School of Medicine, New York City, New York, USA
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Xu Y, Zheng X, Li Y, Ye X, Cheng H, Wang H, Lyu J. Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review. J Evid Based Med 2023; 16:342-375. [PMID: 37718729 DOI: 10.1111/jebm.12548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 08/30/2023] [Indexed: 09/19/2023]
Abstract
BACKGROUND Increasingly, patient medication adherence data are being consolidated from claims databases and electronic health records (EHRs). Such databases offer an indirect avenue to gauge medication adherence in our data-rich healthcare milieu. The surge in data accessibility, coupled with the pressing need for its conversion to actionable insights, has spotlighted data mining, with machine learning (ML) emerging as a pivotal technique. Nonadherence poses heightened health risks and escalates medical costs. This paper elucidates the synergistic interaction between medical database mining for medication adherence and the role of ML in fostering knowledge discovery. METHODS We conducted a comprehensive review of EHR applications in the realm of medication adherence, leveraging ML techniques. We expounded on the evolution and structure of medical databases pertinent to medication adherence and harnessed both supervised and unsupervised ML paradigms to delve into adherence and its ramifications. RESULTS Our study underscores the applications of medical databases and ML, encompassing both supervised and unsupervised learning, for medication adherence in clinical big data. Databases like SEER and NHANES, often underutilized due to their intricacies, have gained prominence. Employing ML to excavate patient medication logs from these databases facilitates adherence analysis. Such findings are pivotal for clinical decision-making, risk stratification, and scholarly pursuits, aiming to elevate healthcare quality. CONCLUSION Advanced data mining in the era of big data has revolutionized medication adherence research, thereby enhancing patient care. Emphasizing bespoke interventions and research could herald transformative shifts in therapeutic modalities.
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Affiliation(s)
- Yixian Xu
- Department of Anesthesiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Xinkai Zheng
- Department of Dermatology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yuanjie Li
- Planning & Discipline Construction Office, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Xinmiao Ye
- Department of Anesthesiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Hongtao Cheng
- School of Nursing, Jinan University, Guangzhou, China
| | - Hao Wang
- Department of Anesthesiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jun Lyu
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Guangzhou, China
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Behnoush AH, Khalaji A, Rezaee M, Momtahen S, Mansourian S, Bagheri J, Masoudkabir F, Hosseini K. Machine learning-based prediction of 1-year mortality in hypertensive patients undergoing coronary revascularization surgery. Clin Cardiol 2023; 46:269-278. [PMID: 36588391 PMCID: PMC10018097 DOI: 10.1002/clc.23963] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/12/2022] [Accepted: 12/19/2022] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Machine learning (ML) has shown promising results in all fields of medicine, including preventive cardiology. Hypertensive patients are at higher risk of mortality after coronary artery bypass graft (CABG) surgery; thus, we aimed to design and evaluate five ML models to predict 1-year mortality among hypertensive patients who underwent CABG. HYOTHESIS ML algorithms can significantly improve mortality prediction after CABG. METHODS Tehran Heart Center's CABG data registry was used to extract several baseline and peri-procedural characteristics and mortality data. The best features were chosen using random forest (RF) feature selection algorithm. Five ML models were developed to predict 1-year mortality: logistic regression (LR), RF, artificial neural network (ANN), extreme gradient boosting (XGB), and naïve Bayes (NB). The area under the curve (AUC), sensitivity, and specificity were used to evaluate the models. RESULTS Among the 8,493 hypertensive patients who underwent CABG (mean age of 68.27 ± 9.27 years), 303 died in the first year. Eleven features were selected as the best predictors, among which total ventilation hours and ejection fraction were the leading ones. LR showed the best prediction ability with an AUC of 0.82, while the least AUC was for the NB model (0.79). Among the subgroups, the highest AUC for LR model was for two age range groups (50-59 and 80-89 years), overweight, diabetic, and smoker subgroups of hypertensive patients. CONCLUSIONS All ML models had excellent performance in predicting 1-year mortality among CABG hypertension patients, while LR was the best regarding AUC. These models can help clinicians assess the risk of mortality in specific subgroups at higher risk (such as hypertensive ones).
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Affiliation(s)
- Amir Hossein Behnoush
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Amirmohammad Khalaji
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Malihe Rezaee
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.,School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Shahram Momtahen
- Department of Surgery, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Soheil Mansourian
- Department of Surgery, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Jamshid Bagheri
- Department of Surgery, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Farzad Masoudkabir
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Kaveh Hosseini
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
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9
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Kanyongo W, Ezugwu AE. Machine learning approaches to medication adherence amongst NCD patients: A systematic literature review. INFORMATICS IN MEDICINE UNLOCKED 2023. [DOI: 10.1016/j.imu.2023.101210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023] Open
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10
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Kanyongo W, Ezugwu AE. Feature selection and importance of predictors of non-communicable diseases medication adherence from machine learning research perspectives. INFORMATICS IN MEDICINE UNLOCKED 2023. [DOI: 10.1016/j.imu.2023.101232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023] Open
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Li M, Lu X, Yang H, Yuan R, Yang Y, Tong R, Wu X. Development and assessment of novel machine learning models to predict medication non-adherence risks in type 2 diabetics. Front Public Health 2022; 10:1000622. [PMID: 36466490 PMCID: PMC9714465 DOI: 10.3389/fpubh.2022.1000622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 10/24/2022] [Indexed: 11/18/2022] Open
Abstract
Background Medication adherence is the main determinant of effective management of type 2 diabetes, yet there is no gold standard method available to screen patients with high-risk non-adherence. Developing machine learning models to predict high-risk non-adherence in patients with T2D could optimize management. Methods This cross-sectional study was carried out on patients with T2D at the Sichuan Provincial People's Hospital from April 2018 to December 2019 who were examined for HbA1c on the day of the survey. Demographic and clinical characteristics were extracted from the questionnaire and electronic medical records. The sample was randomly divided into a training dataset and a test dataset with a radio of 8:2 after data preprocessing. Four imputing methods, five sampling methods, three screening methods, and 18 machine learning algorithms were used to groom data and develop and validate models. Bootstrapping was performed to generate the validation set for external validation and univariate analysis. Models were compared on the basis of predictive performance metrics. Finally, we validated the sample size on the best model. Results This study included 980 patients with T2D, of whom 184 (18.8%) were defined as medication non-adherence. The results indicated that the model used modified random forest as the imputation method, random under sampler as the sampling method, Boruta as the feature screening method and the ensemble algorithms and had the best performance. The area under the receiver operating characteristic curve (AUC), F1 score, and area under the precision-recall curve (AUPRC) of the best model, among a total of 1,080 trained models, were 0.8369, 0.7912, and 0.9574, respectively. Age, present fasting blood glucose (FBG) values, present HbA1c values, present random blood glucose (RBG) values, and body mass index (BMI) were the most significant contributors associated with risks of medication adherence. Conclusion We found that machine learning methods could be used to predict the risk of non-adherence in patients with T2D. The proposed model was well performed to identify patients with T2D with non-adherence and could help improve individualized T2D management.
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Affiliation(s)
- Mengting Li
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China,Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China
| | - Xiangyu Lu
- Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China,The Second Department of Hepatobiliary Surgery, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - HengBo Yang
- School of Pharmacy, Chengdu Medical College, Chengdu, China
| | - Rong Yuan
- Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China,Endocrine Department, Sichuan Provincial People's Hospital, Chengdu, China
| | - Yong Yang
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China,Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China,*Correspondence: Yong Yang
| | - Rongsheng Tong
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China,Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China,Rongsheng Tong
| | - Xingwei Wu
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China,Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China,Xingwei Wu
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12
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Błaziak M, Urban S, Wietrzyk W, Jura M, Iwanek G, Stańczykiewicz B, Kuliczkowski W, Zymliński R, Pondel M, Berka P, Danel D, Biegus J, Siennicka A. An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review. Biomedicines 2022; 10:biomedicines10092188. [PMID: 36140289 PMCID: PMC9496386 DOI: 10.3390/biomedicines10092188] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 08/19/2022] [Accepted: 08/27/2022] [Indexed: 11/23/2022] Open
Abstract
Heart failure (HF) is one of the leading causes of mortality and hospitalization worldwide. The accurate prediction of mortality and readmission risk provides crucial information for guiding decision making. Unfortunately, traditional predictive models reached modest accuracy in HF populations. We therefore aimed to present predictive models based on machine learning (ML) techniques in HF patients that were externally validated. We searched four databases and the reference lists of the included papers to identify studies in which HF patient data were used to create a predictive model. Literature screening was conducted in Academic Search Ultimate, ERIC, Health Source Nursing/Academic Edition and MEDLINE. The protocol of the current systematic review was registered in the PROSPERO database with the registration number CRD42022344855. We considered all types of outcomes: mortality, rehospitalization, response to treatment and medication adherence. The area under the receiver operating characteristic curve (AUC) was used as the comparator parameter. The literature search yielded 1649 studies, of which 9 were included in the final analysis. The AUCs for the machine learning models ranged from 0.6494 to 0.913 in independent datasets, whereas the AUCs for statistical predictive scores ranged from 0.622 to 0.806. Our study showed an increasing number of ML predictive models concerning HF populations, although external validation remains infrequent. However, our findings revealed that ML approaches can outperform conventional risk scores and may play important role in HF management.
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Affiliation(s)
- Mikołaj Błaziak
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
- Correspondence: (M.B.); (W.K.); Tel.: +48-71-733-11-12 (M.B.)
| | - Szymon Urban
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
| | - Weronika Wietrzyk
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
| | - Maksym Jura
- Department of Physiology and Pathophysiology, Wroclaw Medical University, 50-368 Wroclaw, Poland
| | - Gracjan Iwanek
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
| | - Bartłomiej Stańczykiewicz
- Department of Psychiatry, Division of Consultation Psychiatry and Neuroscience, Wroclaw Medical University, 50-367 Wroclaw, Poland
| | - Wiktor Kuliczkowski
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
- Correspondence: (M.B.); (W.K.); Tel.: +48-71-733-11-12 (M.B.)
| | - Robert Zymliński
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
| | - Maciej Pondel
- Institute of Information Systems in Economics, Wroclaw University of Economics and Business, 53-345 Wroclaw, Poland
| | - Petr Berka
- Department of Information and Knowledge Engineering, Prague University of Economics and Business, W. Churchill Sq. 1938/4, 130 67 Prague, Czech Republic
| | - Dariusz Danel
- Department of Anthropology, Ludwik Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, 53-114 Wroclaw, Poland
| | - Jan Biegus
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
| | - Agnieszka Siennicka
- Department of Physiology and Pathophysiology, Wroclaw Medical University, 50-368 Wroclaw, Poland
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Khalaji A, Behnoush AH, Jameie M, Sharifi A, Sheikhy A, Fallahzadeh A, Sadeghian S, Pashang M, Bagheri J, Ahmadi Tafti SH, Hosseini K. Machine learning algorithms for predicting mortality after coronary artery bypass grafting. Front Cardiovasc Med 2022; 9:977747. [PMID: 36093147 PMCID: PMC9448905 DOI: 10.3389/fcvm.2022.977747] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 08/02/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundAs the era of big data analytics unfolds, machine learning (ML) might be a promising tool for predicting clinical outcomes. This study aimed to evaluate the predictive ability of ML models for estimating mortality after coronary artery bypass grafting (CABG).Materials and methodsVarious baseline and follow-up features were obtained from the CABG data registry, established in 2005 at Tehran Heart Center. After selecting key variables using the random forest method, prediction models were developed using: Logistic Regression (LR), Support Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) algorithms. Area Under the Curve (AUC) and other indices were used to assess the performance.ResultsA total of 16,850 patients with isolated CABG (mean age: 67.34 ± 9.67 years) were included. Among them, 16,620 had one-year follow-up, from which 468 died. Eleven features were chosen to train the models. Total ventilation hours and left ventricular ejection fraction were by far the most predictive factors of mortality. All the models had AUC > 0.7 (acceptable performance) for 1-year mortality. Nonetheless, LR (AUC = 0.811) and XGBoost (AUC = 0.792) outperformed NB (AUC = 0.783), RF (AUC = 0.783), SVM (AUC = 0.738), and KNN (AUC = 0.715). The trend was similar for two-to-five-year mortality, with LR demonstrating the highest predictive ability.ConclusionVarious ML models showed acceptable performance for estimating CABG mortality, with LR illustrating the highest prediction performance. These models can help clinicians make decisions according to the risk of mortality in patients undergoing CABG.
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Affiliation(s)
- Amirmohammad Khalaji
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Hossein Behnoush
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mana Jameie
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
- Non-communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Sharifi
- Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
| | - Ali Sheikhy
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
- Non-communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Aida Fallahzadeh
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
- Non-communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Saeed Sadeghian
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mina Pashang
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Jamshid Bagheri
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyed Hossein Ahmadi Tafti
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Kaveh Hosseini
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
- *Correspondence: Kaveh Hosseini,
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14
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Chai PR, Vaz C, Goodman GR, Albrechta H, Huang H, Rosen RK, Boyer EW, Mayer KH, O'Cleirigh C. Ingestible electronic sensors to measure instantaneous medication adherence: A narrative review. Digit Health 2022; 8:20552076221083119. [PMID: 35251683 PMCID: PMC8891880 DOI: 10.1177/20552076221083119] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 01/30/2022] [Indexed: 12/03/2022] Open
Abstract
Objective Medication nonadherence contributes to significant morbidity and mortality worldwide. While many techniques to measure adherence exist, digital pill systems represent a novel, direct method of measuring adherence and a means of providing instantaneous adherence supports. In this narrative review, we discuss digital pill system research based on clinical trials and qualitative investigations conducted to date and potential future applications of digital pill system in medication adherence measurement. Methods We conducted a literature search in PubMed of English language peer-reviewed articles describing the use of digital pill system for medication adherence measurement between 2000 and 2021. We included all articles that described the deployment of ingestible sensors and those involving qualitative investigations of digital pill system with human subjects. Results A total of 95 articles were found on initial search; 75 were removed based on exclusion criteria. Included articles were categorized as investigations that deployed an ingestible sensor in human populations (n = 18), or those that conducted qualitative work (n = 3). For pilot studies, the mean accuracy of the sensor to successfully detect a medication ingestion event ranged from 68% to 100%. When digital pill systems were deployed in real-world clinical settings, accuracy ranged from 68% to 90% with lower accuracy due to nonadherence to digital pill system technology. Qualitative studies demonstrated that providers and patients perceive the digital pill system as a facilitator for improving adherence and as a potential platform for delivering adherence interventions. Additionally, ingestion data from digital pill system was viewed as useful in facilitating adherence discussions between clinicians and patients. Conclusions This narrative review demonstrates that the use of digital pill system is broadly feasible across multiple disease states including human immunodeficiency virus, hepatitis C infection, solid organ transplants, tuberculosis, schizophrenia, cardiovascular disease, and acute fractures, where adherence is closely linked to significant morbidity and mortality. It also highlights key areas of research that are still needed prior to broad-scale clinical deployment of such systems.
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Affiliation(s)
- Peter R Chai
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA, USA.,The Fenway Institute, Fenway Health, Boston, MA, USA.,Department of Psychosocial Oncology and Palliative Care, Dana Farber Cancer Institute, Boston, MA, USA.,The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Clint Vaz
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Department of Psychosocial Oncology and Palliative Care, Dana Farber Cancer Institute, Boston, MA, USA
| | - Georgia R Goodman
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA, USA.,The Fenway Institute, Fenway Health, Boston, MA, USA.,Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | | | - Henwei Huang
- The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Rochelle K Rosen
- The Centers for Behavioral and Preventive Medicine, The Miriam Hospital, Providence, RI, USA.,Department of Behavioral and Social Sciences, Brown University School of Public Health, Providence, RI, USA
| | - Edward W Boyer
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA, USA.,The Fenway Institute, Fenway Health, Boston, MA, USA
| | - Kenneth H Mayer
- The Fenway Institute, Fenway Health, Boston, MA, USA.,Division of Infectious Diseases, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Conall O'Cleirigh
- The Fenway Institute, Fenway Health, Boston, MA, USA.,Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
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15
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Tornincasa V, Dixon D, Le Masne Q, Martin B, Arnaud L, van Dommelen P, Koledova E. Integrated Digital Health Solutions in the Management of Growth Disorders in Pediatric Patients Receiving Growth Hormone Therapy: A Retrospective Analysis. Front Endocrinol (Lausanne) 2022; 13:882192. [PMID: 35846336 PMCID: PMC9281444 DOI: 10.3389/fendo.2022.882192] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 06/06/2022] [Indexed: 01/31/2023] Open
Abstract
Digital health has seen rapid advancements over the last few years in helping patients and their healthcare professionals better manage treatment for a variety of illnesses, including growth hormone (GH) therapy for growth disorders in children and adolescents. For children and adolescents requiring such therapy, as well as for their parents, the treatment is longitudinal and often involves daily injections plus close progress monitoring; a sometimes daunting task when young children are involved. Here, we describe our experience in offering devices and digital health tools to support GH therapy across some 40 countries. We also discuss how this ecosystem of care has evolved over the years based on learnings and advances in technology. Finally, we offer a glimpse of future planned enhancements and directions for digital health to play a bigger role in better managing conditions treated with GH therapy, as well as model development for adherence prediction. The continued aim of these technologies is to improve clinical decision making and support for GH-treated patients, leading to better outcomes.
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Affiliation(s)
| | - David Dixon
- Ares Trading S.A. (an affiliate of Merck KGaA), Eysins, Switzerland
| | - Quentin Le Masne
- Ares Trading S.A. (an affiliate of Merck KGaA), Eysins, Switzerland
| | - Blaine Martin
- Ares Trading S.A. (an affiliate of Merck KGaA), Eysins, Switzerland
| | - Lilian Arnaud
- Ares Trading S.A. (an affiliate of Merck KGaA), Eysins, Switzerland
| | - Paula van Dommelen
- Department of Child Health, The Netherlands Organization for Applied Scientific Research TNO, Leiden, Netherlands
| | - Ekaterina Koledova
- Global Medical Affairs Cardiometabolic & Endocrinology, Merck Healthcare KGaA, Darmstadt, Germany
- *Correspondence: Ekaterina Koledova,
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16
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Bohlmann A, Mostafa J, Kumar M. Machine Learning and Medication Adherence: Scoping Review. JMIRX MED 2021; 2:e26993. [PMID: 37725549 PMCID: PMC10414315 DOI: 10.2196/26993] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 05/03/2021] [Accepted: 09/14/2021] [Indexed: 09/21/2023]
Abstract
BACKGROUND This is the first scoping review to focus broadly on the topics of machine learning and medication adherence. OBJECTIVE This review aims to categorize, summarize, and analyze literature focused on using machine learning for actions related to medication adherence. METHODS PubMed, Scopus, ACM Digital Library, IEEE, and Web of Science were searched to find works that meet the inclusion criteria. After full-text review, 43 works were included in the final analysis. Information of interest was systematically charted before inclusion in the final draft. Studies were placed into natural categories for additional analysis dependent upon the combination of actions related to medication adherence. The protocol for this scoping review was created using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. RESULTS Publications focused on predicting medication adherence have uncovered 20 strong predictors that were significant in two or more studies. A total of 13 studies that predicted medication adherence used either self-reported questionnaires or pharmacy claims data to determine medication adherence status. In addition, 13 studies that predicted medication adherence did so using either logistic regression, artificial neural networks, random forest, or support vector machines. Of the 15 studies that predicted medication adherence, 6 reported predictor accuracy, the lowest of which was 77.6%. Of 13 monitoring systems, 12 determined medication administration using medication container sensors or sensors in consumer electronics, like smartwatches or smartphones. A total of 11 monitoring systems used logistic regression, artificial neural networks, support vector machines, or random forest algorithms to determine medication administration. The 4 systems that monitored inhaler administration reported a classification accuracy of 93.75% or higher. The 2 systems that monitored medication status in patients with Parkinson disease reported a classification accuracy of 78% or higher. A total of 3 studies monitored medication administration using only smartwatch sensors and reported a classification accuracy of 78.6% or higher. Two systems that provided context-aware medication reminders helped patients to achieve an adherence level of 92% or higher. Two conversational artificial intelligence reminder systems significantly improved adherence rates when compared against traditional reminder systems. CONCLUSIONS Creation of systems that accurately predict medication adherence across multiple data sets may be possible due to predictors remaining strong across multiple studies. Higher quality measures of adherence should be adopted when possible so that prediction algorithms are based on accurate information. Currently, medication adherence can be predicted with a good level of accuracy, potentially allowing for the development of interventions aimed at preventing nonadherence. Monitoring systems that track inhaler use currently classify inhaler-related actions with an excellent level of accuracy, allowing for tracking of adherence and potentially proper inhaler technique. Systems that monitor medication states in patients with Parkinson disease can currently achieve a good level of classification accuracy and have the potential to inform medication therapy changes in the future. Medication administration monitoring systems that only use motion sensors in smartwatches can currently achieve a good level of classification accuracy but only when differentiating between a small number of possible activities. Context-aware reminder systems can help patients achieve high levels of medication adherence but are also intrusive, which may not be acceptable to users. Conversational artificial intelligence reminder systems can significantly improve adherence.
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Affiliation(s)
- Aaron Bohlmann
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Javed Mostafa
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Manish Kumar
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Public Health Leadership Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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17
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Maurya MR, Riyaz NUSS, Reddy MSB, Yalcin HC, Ouakad HM, Bahadur I, Al-Maadeed S, Sadasivuni KK. A review of smart sensors coupled with Internet of Things and Artificial Intelligence approach for heart failure monitoring. Med Biol Eng Comput 2021; 59:2185-2203. [PMID: 34611787 DOI: 10.1007/s11517-021-02447-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 09/01/2021] [Indexed: 02/07/2023]
Abstract
Over the last decade, there has been a huge demand for health care technologies such as sensors-based prediction using digital health. With the continuous rise in the human population, these technologies showed to be potentially effective solutions to life-threatening diseases such as heart failure (HF). Besides being a potential for early death, HF has a significantly reduced quality of life (QoL). Heart failure has no cure. However, treatment can help you live a longer and more active life with fewer symptoms. Thus, it is essential to develop technological aid solutions allowing early diagnosis and consequently, effective treatment with possibly delayed mortality. Commonly, forecasts of HF are based on the generation of vast volumes of data usually collected from an individual patient by different components of the family history, physical examination, basic laboratory results, and other medical records. Though, these data are not effectively useful for predicting this failure, nevertheless, with the aid of advanced medical technology such as interconnected multi-sensory-based devices, and based on several medical history characteristics, the broad data provided machine learning algorithms to predict risk factors for heart disease of an individual is beneficial. There will be many challenges for the next decade of advancements in HF care: exploiting an increasingly growing repertoire of interconnected internal and external sensors for the benefit of patients and processing large, multimodal datasets with new Artificial Intelligence (AI) software. Various methods for predicting heart failure and, primarily the significance of invasive and non-invasive sensors along with different strategies for machine learning to predict heart failure are presented and summarized in the present study.
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Affiliation(s)
- Muni Raj Maurya
- Center for Advanced Materials, Qatar University, P.O. Box 2713, Doha, Qatar
- Department of Mechanical and Industrial Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
| | | | - M Sai Bhargava Reddy
- Center for Nanoscience and Technology, Institute of Science and Technology, Jawaharlal Nehru Technological University, Hyderabad, Telangana State, 500085, India
| | | | - Hassen M Ouakad
- Mechanical and Industrial Engineering Department, College of Engineering, Sultan Qaboos University, Al-Khoudh, 123, PO-BOX 33, Muscat, Oman.
| | - Issam Bahadur
- Mechanical and Industrial Engineering Department, College of Engineering, Sultan Qaboos University, Al-Khoudh, 123, PO-BOX 33, Muscat, Oman
| | - Somaya Al-Maadeed
- Department of Computer Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
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18
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Predicting medication adherence using ensemble learning and deep learning models with large scale healthcare data. Sci Rep 2021; 11:18961. [PMID: 34556746 PMCID: PMC8460813 DOI: 10.1038/s41598-021-98387-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 09/07/2021] [Indexed: 11/24/2022] Open
Abstract
Clinical studies from WHO have demonstrated that only 50–70% of patients adhere properly to prescribed drug therapy. Such adherence failure can impact therapeutic efficacy for the patients in question and compromises data quality around the population-level efficacy of the drug for the indications targeted. In this study, we applied various ensemble learning and deep learning models to predict medication adherence among patients. Our contribution to this endeavour involves targeting the problem of adherence prediction for a particularly challenging class of patients who self-administer injectable medication at home. Our prediction pipeline, based on event history, comprises a connected sharps bin which aims to help patients better manage their condition and improve outcomes. In other words, the efficiency of interventions can be significantly improved by prioritizing the patients who are most likely to be non-adherent. The collected data comprising a rich event feature set may be exploited for the purposes of predicting the status of the next adherence state for individual patients. This paper reports on how this concept can be realized through an investigation using a wide range of ensemble learning and deep learning models on a real-world dataset collected from such a system. The dataset investigated comprises 342,174 historic injection disposal records collected over the course of more than 5 years. A comprehensive comparison of different models is given in this paper. Moreover, we demonstrate that the selected best performer, long short-term memory (LSTM), generalizes well by deploying it in a true future testing dataset. The proposed end-to-end pipeline is capable of predicting patient failure in adhering to their therapeutic regimen with 77.35 % accuracy (Specificity: 78.28 %, Sensitivity: 76.42%, Precision: 77.87%,F1 score: 0.7714, ROC AUC: 0.8390).
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19
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Yerrapragada G, Siadimas A, Babaeian A, Sharma V, O'Neill TJ. Machine Learning to Predict Tamoxifen Nonadherence Among US Commercially Insured Patients With Metastatic Breast Cancer. JCO Clin Cancer Inform 2021; 5:814-825. [PMID: 34383580 DOI: 10.1200/cci.20.00102] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
PURPOSE Adherence to tamoxifen citrate among women diagnosed with metastatic breast cancer can improve survival and minimize recurrence. This study aimed to use real-world data and machine learning (ML) methods to classify tamoxifen nonadherence. METHODS A cohort of women diagnosed with metastatic breast cancer from 2012 to 2017 were identified from IBM MarketScan Commercial Claims and Encounters and Medicare claims databases. Patients with < 80% proportion of days coverage in the year following treatment initiation were classified as nonadherent. Training and internal validation cohorts were randomly generated (4:1 ratio). Clinical procedures, comorbidity, treatment, and health care encounter features in the year before tamoxifen initiation were used to train logistic regression, boosted logistic regression, random forest, and feedforward neural network models and were internally validated on the basis of area under receiver operating characteristic curve. The most predictive ML approach was evaluated to assess feature importance. RESULTS A total of 3,022 patients were included with 40% classified as nonadherent. All models had moderate predictive accuracy. Logistic regression (area under receiver operating characteristic 0.64) was interpreted with 94% sensitivity (95% CI, 89 to 92) and 0.31 specificity (95% CI, 29 to 33). The model accurately classified adherence (negative predictive value 89%) but was nondiscriminate for nonadherence (positive predictive value 48%). Variable importance identified top predictive factors, including age ≥ 55 years and pretreatment procedures (lymphatic nuclear medicine, radiation oncology, and arterial surgery). CONCLUSION ML using baseline administrative data predicts tamoxifen nonadherence. Screening at treatment initiation may support personalized care, improve health outcomes, and minimize cost. Baseline claims may not be sufficient to discriminate adherence. Further validation with enriched longitudinal data may improve model performance.
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Affiliation(s)
- Gayathri Yerrapragada
- School of Computing, Clemson University, Clemson, SC.,Data Science & Services, Diagnostics Information Solutions, Roche Diagnostics, Belmont, CA
| | - Athanasios Siadimas
- Data Science & Services, Diagnostics Information Solutions, Roche Diagnostics, Belmont, CA
| | - Amir Babaeian
- Data Science & Services, Diagnostics Information Solutions, Roche Diagnostics, Belmont, CA
| | - Vishakha Sharma
- Data Science & Services, Diagnostics Information Solutions, Roche Diagnostics, Belmont, CA
| | - Tyler J O'Neill
- Data Science & Services, Diagnostics Information Solutions, Roche Diagnostics, Belmont, CA
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Vellameeran FA, Brindha T. An integrated review on machine learning approaches for heart disease prediction: Direction towards future research gaps. BIO-ALGORITHMS AND MED-SYSTEMS 2021. [DOI: 10.1515/bams-2020-0069] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Abstract
Objectives
To make a clear literature review on state-of-the-art heart disease prediction models.
Methods
It reviews 61 research papers and states the significant analysis. Initially, the analysis addresses the contributions of each literature works and observes the simulation environment. Here, different types of machine learning algorithms deployed in each contribution. In addition, the utilized dataset for existing heart disease prediction models was observed.
Results
The performance measures computed in entire papers like prediction accuracy, prediction error, specificity, sensitivity, f-measure, etc., are learned. Further, the best performance is also checked to confirm the effectiveness of entire contributions.
Conclusions
The comprehensive research challenges and the gap are portrayed based on the development of intelligent methods concerning the unresolved challenges in heart disease prediction using data mining techniques.
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Affiliation(s)
| | - Thomas Brindha
- Department of Information Technology , Noorul Islam Centre for Higher Education , Kanyakumari , India
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Hosni M, Carrillo de Gea JM, Idri A, El Bajta M, Fernández Alemán JL, García-Mateos G, Abnane I. A systematic mapping study for ensemble classification methods in cardiovascular disease. Artif Intell Rev 2020. [DOI: 10.1007/s10462-020-09914-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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Real-time AI prediction for major adverse cardiac events in emergency department patients with chest pain. Scand J Trauma Resusc Emerg Med 2020; 28:93. [PMID: 32917261 PMCID: PMC7488862 DOI: 10.1186/s13049-020-00786-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Accepted: 09/02/2020] [Indexed: 02/07/2023] Open
Abstract
Background A big-data-driven and artificial intelligence (AI) with machine learning (ML) approach has never been integrated with the hospital information system (HIS) for predicting major adverse cardiac events (MACE) in patients with chest pain in the emergency department (ED). Therefore, we conducted the present study to clarify it. Methods In total, 85,254 ED patients with chest pain in three hospitals between 2009 and 2018 were identified. We randomized the patients into a 70%/30% split for ML model training and testing. We used 14 clinical variables from their electronic health records to construct a random forest model with the synthetic minority oversampling technique preprocessing algorithm to predict acute myocardial infarction (AMI) < 1 month and all-cause mortality < 1 month. Comparisons of the predictive accuracies among random forest, logistic regression, support-vector clustering (SVC), and K-nearest neighbor (KNN) models were also performed. Results Predicting MACE using the random forest model produced areas under the curves (AUC) of 0.915 for AMI < 1 month and 0.999 for all-cause mortality < 1 month. The random forest model had better predictive accuracy than logistic regression, SVC, and KNN. We further integrated the AI prediction model with the HIS to assist physicians with decision-making in real time. Validation of the AI prediction model by new patients showed AUCs of 0.907 for AMI < 1 month and 0.888 for all-cause mortality < 1 month. Conclusions An AI real-time prediction model is a promising method for assisting physicians in predicting MACE in ED patients with chest pain. Further studies to evaluate the impact on clinical practice are warranted.
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Wu XW, Yang HB, Yuan R, Long EW, Tong RS. Predictive models of medication non-adherence risks of patients with T2D based on multiple machine learning algorithms. BMJ Open Diabetes Res Care 2020; 8:8/1/e001055. [PMID: 32156739 PMCID: PMC7064141 DOI: 10.1136/bmjdrc-2019-001055] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 01/08/2020] [Accepted: 01/16/2020] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVE Medication adherence plays a key role in type 2 diabetes (T2D) care. Identifying patients with high risks of non-compliance helps individualized management, especially for China, where medical resources are relatively insufficient. However, models with good predictive capabilities have not been studied. This study aims to assess multiple machine learning algorithms and screen out a model that can be used to predict patients' non-adherence risks. METHODS A real-world registration study was conducted at Sichuan Provincial People's Hospital from 1 April 2018 to 30 March 2019. Data of patients with T2D on demographics, disease and treatment, diet and exercise, mental status, and treatment adherence were obtained by face-to-face questionnaires. The medication possession ratio was used to evaluate patients' medication adherence status. Fourteen machine learning algorithms were applied for modeling, including Bayesian network, Neural Net, support vector machine, and so on, and balanced sampling, data imputation, binning, and methods of feature selection were evaluated by the area under the receiver operating characteristic curve (AUC). We use two-way cross-validation to ensure the accuracy of model evaluation, and we performed a posteriori test on the sample size based on the trend of AUC as the sample size increase. RESULTS A total of 401 patients out of 630 candidates were investigated, of which 85 were evaluated as poor adherence (21.20%). A total of 16 variables were selected as potential variables for modeling, and 300 models were built based on 30 machine learning algorithms. Among these algorithms, the AUC of the best capable one was 0.866±0.082. Imputing, oversampling and larger sample size will help improve predictive ability. CONCLUSIONS An accurate and sensitive adherence prediction model based on real-world registration data was established after evaluating data filling, balanced sampling, and so on, which may provide a technical tool for individualized diabetes care.
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Affiliation(s)
- Xing-Wei Wu
- Personalized Drug Therapy Key Laboratory of Sichuan Province, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Department of Pharmacy, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China
| | - Heng-Bo Yang
- School of Pharmacy, Chengdu Medical College, Chengdu, China
| | - Rong Yuan
- Endocrine Department, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China
| | - En-Wu Long
- Personalized Drug Therapy Key Laboratory of Sichuan Province, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Department of Pharmacy, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China
| | - Rong-Sheng Tong
- Personalized Drug Therapy Key Laboratory of Sichuan Province, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Department of Pharmacy, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China
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Pollesello P, Ben Gal T, Bettex D, Cerny V, Comin-Colet J, Eremenko AA, Farmakis D, Fedele F, Fonseca C, Harjola VP, Herpain A, Heringlake M, Heunks L, Husebye T, Ivancan V, Karason K, Kaul S, Kubica J, Mebazaa A, Mølgaard H, Parissis J, Parkhomenko A, Põder P, Pölzl G, Vrtovec B, Yilmaz MB, Papp Z. Short-Term Therapies for Treatment of Acute and Advanced Heart Failure-Why so Few Drugs Available in Clinical Use, Why Even Fewer in the Pipeline? J Clin Med 2019; 8:jcm8111834. [PMID: 31683969 PMCID: PMC6912236 DOI: 10.3390/jcm8111834] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 10/24/2019] [Accepted: 10/28/2019] [Indexed: 01/10/2023] Open
Abstract
Both acute and advanced heart failure are an increasing threat in term of survival, quality of life and socio-economical burdens. Paradoxically, the use of successful treatments for chronic heart failure can prolong life but-per definition-causes the rise in age of patients experiencing acute decompensations, since nothing at the moment helps avoiding an acute or final stage in the elderly population. To complicate the picture, acute heart failure syndromes are a collection of symptoms, signs and markers, with different aetiologies and different courses, also due to overlapping morbidities and to the plethora of chronic medications. The palette of cardio- and vasoactive drugs used in the hospitalization phase to stabilize the patient's hemodynamic is scarce and even scarcer is the evidence for the agents commonly used in the practice (e.g. catecholamines). The pipeline in this field is poor and the clinical development chronically unsuccessful. Recent set backs in expected clinical trials for new agents in acute heart failure (AHF) (omecamtiv, serelaxine, ularitide) left a field desolately empty, where only few drugs have been approved for clinical use, for example, levosimendan and nesiritide. In this consensus opinion paper, experts from 26 European countries (Austria, Belgium, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Israel, Italy, The Netherlands, Norway, Poland, Portugal, Russia, Slovenia, Spain, Sweden, Switzerland, Turkey, U.K. and Ukraine) analyse the situation in details also by help of artificial intelligence applied to bibliographic searches, try to distil some lesson-learned to avoid that future projects would make the same mistakes as in the past and recommend how to lead a successful development project in this field in dire need of new agents.
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Affiliation(s)
| | - Tuvia Ben Gal
- Heart Failure Unit, Rabin Medical Center, Tel Aviv University, Petah Tikva 4941492d, Israel.
| | - Dominique Bettex
- Institute of Anaesthesiology, University Hospital of Zurich, University of Zurich, 8091 Zurich, Switzerland.
| | - Vladimir Cerny
- Department of Anesthesiology, Perioperative Medicine and Intensive Care, Masaryk Hospital, J.E. Purkinje University, 400 96 Usti nad Labem, Czech Republic.
| | - Josep Comin-Colet
- Heart Diseases Institute, Hospital Universitari de Bellvitge, 08015 Barcelona, Spain.
| | - Alexandr A Eremenko
- Department of Cardiac Intensive Care, Petrovskii National Research Centre of Surgery, Sechenov University, 119146 Moscow, Russia.
| | - Dimitrios Farmakis
- Department of Cardiology, Medical School, University of Cyprus, 1678 Nicosia, Cyprus.
| | - Francesco Fedele
- Department of Cardiovascular, Respiratory, Nephrology, Anesthesiology and Geriatric Sciences, 'La Sapienza' University of Rome, 00185 Rome, Italy.
| | - Cândida Fonseca
- Heart Failure Clinic of S. Francisco Xavier Hospital, CHLO, 1449-005 Lisbon, Portugal.
| | - Veli-Pekka Harjola
- Emergency Medicine, Department of Emergency Medicine and Services, Helsinki University Hospital, University of Helsinki, 00014 Helsinki, Finland.
| | - Antoine Herpain
- Department of Intensive Care, Experimental Laboratory of Intensive Care, Erasme Hospital, Université Libre de Bruxelles, 1050 Bruxelles, Belgium.
| | - Matthias Heringlake
- Department of Anesthesiology and Intensive Care Medicine, University of Lübeck, 23562 Lübeck, Germany.
| | - Leo Heunks
- Department of Intensive Care Medicine, Amsterdam UMC, location VUmc 081 HV, The Netherlands.
| | - Trygve Husebye
- Department of Cardiology, Oslo University Hospital Ullevaal, 0372 Oslo, Norway.
| | - Visnja Ivancan
- Department of Anesthesiology, Reanimatology and Intensive Care, University Hospital Centre, 10000 Zagreb, Croatia.
| | - Kristian Karason
- Transplant Institute, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden.
| | - Sundeep Kaul
- Intensive Care Unit, National Health Service, Leeds LS2 9JT, UK.
| | - Jacek Kubica
- Department of Cardiology and Internal Medicine, Nicolaus Copernicus University, 87-100 Torun, Poland.
| | - Alexandre Mebazaa
- Department of Anaesthesiology and Critical Care Medicine, AP-HP, Saint Louis and Lariboisière University Hospitals, Université de Paris and INSERM UMR-S 942-MASCOT, 75010 Paris, France.
| | - Henning Mølgaard
- Department of Cardiology, Århus University Hospital, 8200 Århus, Denmark.
| | - John Parissis
- Emergency Department, Attikon University Hospital, National and Kapodistrian University of Athens, 157 72 Athens, Greece.
| | - Alexander Parkhomenko
- Emergency Cardiology Department, National Scientific Center M.D. Strazhesko Institute of Cardiology, 02000 Kiev, Ukraine.
| | - Pentti Põder
- Department of Cardiology, North Estonia Medical Center, 13419 Tallinn, Estonia.
| | - Gerhard Pölzl
- Department of Internal Medicine III, Cardiology and Angiology, Medical University of Innsbruck, 6020 Innsbruck, Austria.
| | - Bojan Vrtovec
- Advanced Heart Failure and Transplantation Center, Department of Cardiology, Ljubljana University Medical Center, SI-1000 Ljubljana, Slovenia.
| | - Mehmet B Yilmaz
- Department of Cardiology, Dokuz Eylul University Faculty of Medicine, 35340 Izmir, Turkey.
| | - Zoltan Papp
- Division of Clinical Physiology, Department of Cardiology, Faculty of Medicine, University of Debrecen, H-4032 Debrecen, Hungary.
- HAS-UD Vascular Biology and Myocardial Pathophysiology Research Group, Hungarian Academy of Sciences, 4001 Debrecen, Hungary.
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da Silva VJ, da Silva Souza V, Guimarães da Cruz R, Mesquita Vidal Martinez de Lucena J, Jazdi N, Ferreira de Lucena Junior V. Commercial Devices-Based System Designed to Improve the Treatment Adherence of Hypertensive Patients. SENSORS (BASEL, SWITZERLAND) 2019; 19:E4539. [PMID: 31635394 PMCID: PMC6832274 DOI: 10.3390/s19204539] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 10/09/2019] [Accepted: 10/16/2019] [Indexed: 01/05/2023]
Abstract
This paper presents an intelligent system designed to increase the treatment adherence of hypertensive patients. The architecture was developed to allow communication among patients, physicians, and families to determine each patient's rate assertion of medication intake time and their self-monitoring of blood pressure. Concerning the medication schedule, the system is designed to follow a predefined prescription, adapting itself to undesired events, such as mistakenly taking medication or forgetting to take medication on time. When covering the blood pressure measurement, it incorporates best medical practices, registering the actual values in recommended frequency and form, trying to avoid the known "white-coat effect." We assume that taking medicine precisely and measuring blood pressure correctly may lead to good adherence to the treatment. The system uses commercial consumer electronic devices and can be replicated in any home equipped with a standard personal computer and Internet access. The resulting architecture has four layers. The first is responsible for adding electronic devices that typically exist in today's homes to the system. The second is a preprocessing layer that filters the data generated from the patient's behavior. The third is a reasoning layer that decides how to act based on the patient's activities observed. Finally, the fourth layer creates messages that should drive the reactions of all involved actors. The reasoning layer takes into consideration the patient's schedule and medication-taking activity data and uses implicit algorithms based on the J48, RepTree, and RandomTree decision tree models to infer the adherence. The algorithms were first adjusted using one academic machine learning and data mining tool. The system communicates with users through smartphones (anytime and anywhere) and smart TVs (in the patient's home) by using the 3G/4G and WiFi infrastructure. It interacts automatically through social networks with doctors and relatives when changes or mistakes in medication intake and blood pressure mean values are detected. By associating the blood pressure data with the history of medication intake, our system can indicate the treatment adherence and help patients to achieve better treatment results. Comparisons with similar research were made, highlighting our findings.
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Affiliation(s)
| | | | | | | | - Nasser Jazdi
- Institute of Industrial Automation and Software Systems, The University of Stuttgart, 70174 Stuttgart, Germany.
| | - Vicente Ferreira de Lucena Junior
- Federal University of Amazonas, UFAM-PPGI, Manaus-Amazonas 69067-005, Brazil.
- Federal University of Amazonas, UFAM-PPGEE, Manaus-Amazonas 69067-005, Brazil.
- Prof. Nilmar Lins Pimenta Building, Sector North of UFAM's Main Campus, Technology College, Federal University of Amazonas, UFAM-CETELI, Manaus-Amazonas CEP 69077-00, Brazil.
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26
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Tripoliti EE, Karanasiou GS, Kalatzis FG, Bechlioulis A, Goletsis Y, Naka K, Fotiadis DI. HEARTEN KMS - A knowledge management system targeting the management of patients with heart failure. J Biomed Inform 2019; 94:103203. [PMID: 31071455 DOI: 10.1016/j.jbi.2019.103203] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 05/03/2019] [Accepted: 05/04/2019] [Indexed: 11/19/2022]
Abstract
The aim of this work is to present the HEARTEN Knowledge Management System, one of the core modules of the HEARTEN platform. The HEARTEN platform is an mHealth collaborative environment enabling the Heart Failure patients to self-manage the disease and remain adherent, while allowing the other ecosystem actors (healthcare professionals, caregivers, nutritionists, physical activity experts, psychologists) to monitor the patient's health progress and offer personalized, predictive and preventive disease management. The HEARTEN Knowledge Management System is a tool which provides multiple functionalities to the ecosystem actors for the assessment of the patient's condition, the estimation of the patient's adherence, the prediction of potential adverse events, the calculation of Heart Failure related scores, the extraction of statistics, the association of patient clinical and non-clinical data and the provision of alerts and suggestions. The innovation of this tool lays in the analysis of multi-parametric personal data coming from different sources, including for the first time breath and saliva biomarkers, and the use of machine learning techniques. The HEARTEN Knowledge Management System consists of nine modules. The accuracy of the KMS modules ranges from 78% to 95% depending on the module/functionality.
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Affiliation(s)
- Evanthia E Tripoliti
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, GR 45110 Ioannina, Greece
| | - Georgia S Karanasiou
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, GR 45110 Ioannina, Greece
| | - Fanis G Kalatzis
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, GR 45110 Ioannina, Greece
| | - Aris Bechlioulis
- 2(nd) Department of Cardiology, University of Ioannina, GR 45110 Ioannina, Greece.
| | - Yorgos Goletsis
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, GR 45110 Ioannina, Greece; Department of Economics, University of Ioannina, GR 45110 Ioannina, Greece.
| | - Katerina Naka
- 2(nd) Department of Cardiology, University of Ioannina, GR 45110 Ioannina, Greece
| | - Dimitrios I Fotiadis
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, GR 45110 Ioannina, Greece; Department of Materials Science and Engineering, Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, GR 45110 Ioannina, Greece.
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Román-Villarán E, Pérez-Leon FP, Escobar-Rodriguez GA, Parra-Calderón CL. EIP on AHA Ontology for adherence: Knowledge representation advanced tools. Transl Med UniSa 2019; 19:49-53. [PMID: 31360667 PMCID: PMC6581489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Nowadays diseases tend to chronicle, mainly due to the increase in life expectancy and this leads to a state of polypharmacy. More than 1.5% of Spain's GDP is spent on pharmaceuticals and healthcare products. Complex chronic patients (pluripathological and polymedicated) account for most of the expenditure. The "Action Group A1" of the European Innovation Partnership develops in the "Active and Healthy Ageing" programme actions to improve the quality of life and health outcomes of these patients. On the other hand, the PITeS TIiSS project develops decision support tools to improve this scenario. An ontology has been developed as a tool on adherence. The domain of this ontology is mainly focused on medication adherence and measurement methods. This ontology gathers the necessary knowledge about the domain allowing the use of the ontology as part for is possible.
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Affiliation(s)
- E Román-Villarán
- Biomedical Informatics, Biomedical Engineering and Health Economy R&I Group. Institute of Biomedicine of Seville, IBiS/”Virgen del Rocío” University Hospital/CSIC/University of Seville, Seville, Spain
| | - FP Pérez-Leon
- Biomedical Informatics, Biomedical Engineering and Health Economy R&I Group. Institute of Biomedicine of Seville, IBiS/”Virgen del Rocío” University Hospital/CSIC/University of Seville, Seville, Spain
| | - GA Escobar-Rodriguez
- Biomedical Informatics, Biomedical Engineering and Health Economy R&I Group. Institute of Biomedicine of Seville, IBiS/”Virgen del Rocío” University Hospital/CSIC/University of Seville, Seville, Spain
| | - CL Parra-Calderón
- Biomedical Informatics, Biomedical Engineering and Health Economy R&I Group. Institute of Biomedicine of Seville, IBiS/”Virgen del Rocío” University Hospital/CSIC/University of Seville, Seville, Spain,Head of Innovation Technology, “Virgen del Rocío” University Hospital, Seville, Spain
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Meenachi L, Ramakrishnan S. Evolutionary sequential genetic search technique-based cancer classification using fuzzy rough nearest neighbour classifier. Healthc Technol Lett 2018; 5:130-135. [PMID: 30155265 PMCID: PMC6103784 DOI: 10.1049/htl.2018.5041] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Accepted: 06/29/2018] [Indexed: 11/30/2022] Open
Abstract
Cancer is one of the deadly diseases of human life. The patient may likely to survive if the disease is diagnosed in its early stages. In this Letter, the authors propose a genetic search fuzzy rough (GSFR) feature selection algorithm, which is hybridised using the evolutionary sequential genetic search technique and fuzzy rough set to select features. The genetic operator's selection, crossover and mutation are applied to generate the subset of features from dataset. The generated subset is subjected to the evaluation with the modified dependency function of the fuzzy rough set using positive and boundary regions, which act as a fitness function. The generation and evaluation of the subset of features continue until the best subset is arrived at to develop the classification model. Selected features are applied to the different classifiers, from the classifiers fuzzy-rough nearest neighbour (FRNN) classifier, which outperforms in terms of classification accuracy and computation time. Hence, the FRNN is applied for performance analysis of existing feature selection algorithms against the proposed GSFR feature selection algorithm. The result generated from the proposed GSFR feature selection algorithm proved to be precise when compared to other feature selection algorithms.
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Affiliation(s)
- Loganathan Meenachi
- Department of Information Technology, Dr.Mahalingam College of Engineering and Technology, Pollachi, Tamil Nadu, India
| | - Srinivasan Ramakrishnan
- Department of Information Technology, Dr.Mahalingam College of Engineering and Technology, Pollachi, Tamil Nadu, India
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30
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Sau A, Bhakta I. Predicting anxiety and depression in elderly patients using machine learning technology. Healthc Technol Lett 2017. [DOI: 10.1049/htl.2016.0096] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Arkaprabha Sau
- Department of Community MedicineR.G. Kar Medical College and HospitalKolkata700 004India
| | - Ishita Bhakta
- Department of Computer Science and EngineeringWest Bengal University of TechnologyKolkata700064India
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31
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Tripoliti EE, Papadopoulos TG, Karanasiou GS, Kalatzis FG, Goletsis Y, Bechlioulis A, Ghimenti S, Lomonaco T, Bellagambi F, Trivella MG, Fuoco R, Marzilli M, Scali MC, Naka KK, Errachid A, Fotiadis DI. A computational approach for the estimation of heart failure patients status using saliva biomarkers. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:3648-3651. [PMID: 29060689 DOI: 10.1109/embc.2017.8037648] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The aim of this work is to present a computational approach for the estimation of the severity of heart failure (HF) in terms of New York Heart Association (NYHA) class and the characterization of the status of the HF patients, during hospitalization, as acute, progressive or stable. The proposed method employs feature selection and classification techniques. However, it is differentiated from the methods reported in the literature since it exploits information that biomarkers fetch. The method is evaluated on a dataset of 29 patients, through a 10-fold-cross-validation approach. The accuracy is 94 and 77% for the estimation of HF severity and the status of HF patients during hospitalization, respectively.
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