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Ojo S, Okoye TO, Olaniyi SA, Ofochukwu VC, Obi MO, Nwokolo AS, Okeke-Moffatt C, Iyun OB, Idemudia EA, Obodo OR, Mokwenye VC, Okobi OE. Ensuring Continuity of Care: Effective Strategies for the Post-hospitalization Transition of Psychiatric Patients in a Family Medicine Outpatient Clinic. Cureus 2024; 16:e52263. [PMID: 38352099 PMCID: PMC10863747 DOI: 10.7759/cureus.52263] [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] [Accepted: 01/14/2024] [Indexed: 02/16/2024] Open
Abstract
In healthcare, continuity of care is a crucial element, especially for patients in the field of psychiatry who have recently been discharged from a hospital. The shift from inpatient to outpatient care poses challenges for patients and healthcare providers, including openness to treatment, competing priorities, financial insecurity, concerns and dilemmas faced by patients regarding their post-hospitalization life after improvements in symptoms, lack of social support, poor patient-doctor relationships, lack of insight, and stigma associated with mental illness. Therefore, it is vital to employ effective strategies to ensure patients receive the required care and support during this transition. This review delves into the significance of continuity of care for psychiatric patients post-hospitalization, effective strategies for the transition, and the challenges and barriers to implementation from the perspective of a family medicine practice. To analyze physicians' role in managing psychiatric patients post-hospitalization, we developed a comprehensive search strategy. This involved extracting relevant data, updates, guidelines, and recommendations. Our search spanned various online repositories, such as PubMed and Google Scholar, specifically focusing on US-based guidelines aligned with our objectives. The search was conducted using medical subject headings (MeSH) and combinations of "OR," "AND," and "WITH." We crafted keywords to optimize our search strategy, including psychiatric illness, post-hospitalization, follow-up, follow-up care, primary care follow-up, and guidelines. Exploring online repositories yielded 132 articles, and we identified some guidelines that addressed our objectives. We established inclusion and exclusion criteria for our review and reviewed 21 papers. Post-hospitalization follow-up is a critical facet of psychiatric care, aligning with guidelines from the American Psychiatric Association and other relevant sources. Emphasizing continuity of care ensures a smooth transition from inpatient to outpatient settings, sustaining therapeutic momentum and minimizing the risk of relapse. This comprehensive approach involves careful medication management, regular mental health assessments, education on condition-specific coping strategies, and coordinated care between healthcare providers. It includes conducting risk assessments, safety planning, building social support and community integration, prompt post-hospitalization follow-up, and tailored treatment plans. Together, these measures enhance overall wellness for recently discharged patients. This holistic strategy tackles pressing short-term needs while facilitating long-term stability, promoting resilience and successful community reintegration, reducing readmission likelihood, and ultimately supporting sustained recovery.
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Affiliation(s)
- Soji Ojo
- Psychiatry, University of Texas Health Science Center at Houston, Dallas, USA
| | | | - Seyi A Olaniyi
- Medicine and Surgery, Obafemi Awolowo University, Ile-Ife, NGA
| | - Victor C Ofochukwu
- Medicine, Ebonyi State University, Abakaliki, NGA
- Medicine and Surgery, Hospital Corporation of America (HCA) Houston Healthcare Pearland, Pearland, USA
| | - Maureen O Obi
- General Practice, Federal Teaching Hospital, Owerri, NGA
| | | | - Chinwe Okeke-Moffatt
- Psychiatry and Behavioral Sciences, Washington University School of Health Sciences, San Pedro, BLZ
| | - Oluwatosin B Iyun
- School of Public Health and Family Medicine, University of Cape Town, Cape Town, ZAF
| | - Etinosa A Idemudia
- Psychiatry and Behavioral Sciences, North Vista Hospital, Las Vegas, USA
| | | | - Violet C Mokwenye
- General Practice, University of Uyo, Uyo, NGA
- General Practice, National Hospital Abuja, Abuja, NGA
| | - Okelue E Okobi
- Family Medicine, Larkin Community Hospital Palm Springs Campus, Miami, USA
- Family Medicine, Medficient Health Systems, Laurel, USA
- Family Medicine, Lakeside Medical center, Belle Glade, USA
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Palacios-Ariza MA, Morales-Mendoza E, Murcia J, Arias-Duarte R, Lara-Castellanos G, Cely-Jiménez A, Rincón-Acuña JC, Araúzo-Bravo MJ, McDouall J. Prediction of patient admission and readmission in adults from a Colombian cohort with bipolar disorder using artificial intelligence. Front Psychiatry 2023; 14:1266548. [PMID: 38179255 PMCID: PMC10764573 DOI: 10.3389/fpsyt.2023.1266548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 11/30/2023] [Indexed: 01/06/2024] Open
Abstract
Introduction Bipolar disorder (BD) is a chronically progressive mental condition, associated with a reduced quality of life and greater disability. Patient admissions are preventable events with a considerable impact on global functioning and social adjustment. While machine learning (ML) approaches have proven prediction ability in other diseases, little is known about their utility to predict patient admissions in this pathology. Aim To develop prediction models for hospital admission/readmission within 5 years of diagnosis in patients with BD using ML techniques. Methods The study utilized data from patients diagnosed with BD in a major healthcare organization in Colombia. Candidate predictors were selected from Electronic Health Records (EHRs) and included sociodemographic and clinical variables. ML algorithms, including Decision Trees, Random Forests, Logistic Regressions, and Support Vector Machines, were used to predict patient admission or readmission. Survival models, including a penalized Cox Model and Random Survival Forest, were used to predict time to admission and first readmission. Model performance was evaluated using accuracy, precision, recall, F1 score, area under the receiver operating characteristic curve (AUC) and concordance index. Results The admission dataset included 2,726 BD patients, with 354 admissions, while the readmission dataset included 352 patients, with almost half being readmitted. The best-performing model for predicting admission was the Random Forest, with an accuracy score of 0.951 and an AUC of 0.98. The variables with the greatest predictive power in the Recursive Feature Elimination (RFE) importance analysis were the number of psychiatric emergency visits, the number of outpatient follow-up appointments and age. Survival models showed similar results, with the Random Survival Forest performing best, achieving an AUC of 0.95. However, the prediction models for patient readmission had poorer performance, with the Random Forest model being again the best performer but with an AUC below 0.70. Conclusion ML models, particularly the Random Forest model, outperformed traditional statistical techniques for admission prediction. However, readmission prediction models had poorer performance. This study demonstrates the potential of ML techniques in improving prediction accuracy for BD patient admissions.
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Affiliation(s)
| | - Esteban Morales-Mendoza
- Fundación Universitaria Sanitas, Gerencia y Gestión Sanitaria Research Group, Instituto de Gerencia y Gestión Sanitaria (IGGS), Bogotá, Colombia
| | - Jossie Murcia
- Fundación Universitaria Sanitas, Gerencia y Gestión Sanitaria Research Group, Instituto de Gerencia y Gestión Sanitaria (IGGS), Bogotá, Colombia
| | - Rafael Arias-Duarte
- Psicopatología y Sociedad Research Group, Facultad de Medicina, Fundación Universitaria Sanitas, Bogotá, Colombia
| | - Germán Lara-Castellanos
- Psicopatología y Sociedad Research Group, Facultad de Medicina, Fundación Universitaria Sanitas, Bogotá, Colombia
| | | | | | - Marcos J. Araúzo-Bravo
- Keralty, Bogotá, Colombia
- Computational Biology and Systems Biomedicine, Biodonostia Health Research Institute, San Sebastián, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
- Department of Cell Biology and Histology, Faculty of Medicine and Nursing, University of Basque Country (UPV/EHU), Leioa, Spain
| | - Jorge McDouall
- Sanitas Crea Research Group, Fundación Universitaria Sanitas, Bogotá, Colombia
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Okagbue HI, Ijezie OA, Ugwoke PO, Adeyemi-Kayode TM, Jonathan O. Single-label machine learning classification revealed some hidden but inter-related causes of five psychotic disorder diseases. Heliyon 2023; 9:e19422. [PMID: 37674848 PMCID: PMC10477489 DOI: 10.1016/j.heliyon.2023.e19422] [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: 11/21/2022] [Revised: 08/04/2023] [Accepted: 08/22/2023] [Indexed: 09/08/2023] Open
Abstract
Psychotic disorder diseases (PDD) or mental illnesses are group of illnesses that affect the minds and impair the cognitive ability, retard emotional ability and obstruct the process of communication and relationship with others and are characterized by delusions, hallucinations and disoriented or disordered pattern of thinking. Prognosis of PDD is not sufficient because of the nature of the diseases and as such adequate form of diagnosis is required to detect, manage and treat the illness. This paper applied the single-label classification (SLC) machine learning approach in mining of electronic health records of people with PDD in Nigeria using eleven independent (demographic) variables and five PDD as target variables. The five PDDs are Insomnia, Schizophrenia, Minimal Brain dysfunction (MBD), which is also known as Attention-Deficit/Hyperactivity Disorder (ADHD), Vascular Dementia (VD) and Bipolar Disorder (BD). The aim of using SLC is that it would be easier to detect some PDDs that are related to each other without the loss of information, which is a plus over multi-label classification (MLC). ReliefF algorithm was used at each experiment to precipitate the order of importance of the independent variables and redundant variables were excluded from the analysis. The order of the variables in feature selection was matched with feature importance after the classifications and quantified using the Spearman rank correlation coefficient. The data was divided into: 70% for training and 30% for testing. Four new performance metrics adapted from the root mean square (RMSE) were proposed and used to measure the differences between the performance results of the 10 Machine learning models in terms of the training and testing and secondly, feature and without feature selection. The new metrics are close to zero which is an indication that the use of feature selection and cross validation may not greatly affects the accuracy of the SLC. When the PDDs are included as predictors for classifying others, there was a tremendous improvement as revealed by the four new metrics for classification accuracy (CA), precision and recall. Analysis of variance showed the four different metrics differs significantly for classification accuracy (CA) and precision. However, there were no significant difference between the CA and precision when the duo are compared together across the four evaluation metrics at p value less than 0.05.
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Affiliation(s)
| | - Ogochukwu A. Ijezie
- Faculty of Science and Technology, Bournemouth University, Poole, BH12 5BB, UK
| | - Paulinus O. Ugwoke
- Department of Computer Science, University of Nigeria, Nsukka, Nigeria
- Digital Bridge Institute, International Centre for Information & Communications Technology Studies, Abuja, Nigeria
| | | | - Oluranti Jonathan
- Department of Computer & Information Sciences, Covenant University, Ota, Nigeria
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Góngora Alonso S, Herrera Montano I, Ayala JLM, Rodrigues JJPC, Franco-Martín M, de la Torre Díez I. Machine Learning Models to Predict Readmission Risk of Patients with Schizophrenia in a Spanish Region. Int J Ment Health Addict 2023. [DOI: 10.1007/s11469-022-01001-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
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Lee W, Schwartz N, Bansal A, Khor S, Hammarlund N, Basu A, Devine B. A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 2-Data From Nonwearables. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2022; 25:2053-2061. [PMID: 35989154 DOI: 10.1016/j.jval.2022.07.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 06/10/2022] [Accepted: 07/10/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVES Despite the increasing interest in applying machine learning (ML) methods in health economics and outcomes research (HEOR), stakeholders face uncertainties in when and how ML can be used. We reviewed the recent applications of ML in HEOR. METHODS We searched PubMed for studies published between January 2020 and March 2021 and randomly chose 20% of the identified studies for the sake of manageability. Studies that were in HEOR and applied an ML technique were included. Studies related to wearable devices were excluded. We abstracted information on the ML applications, data types, and ML methods and analyzed it using descriptive statistics. RESULTS We retrieved 805 articles, of which 161 (20%) were randomly chosen. Ninety-two of the random sample met the eligibility criteria. We found that ML was primarily used for predicting future events (86%) rather than current events (14%). The most common response variables were clinical events or disease incidence (42%) and treatment outcomes (22%). ML was less used to predict economic outcomes such as health resource utilization (16%) or costs (3%). Although electronic medical records (35%) were frequently used for model development, claims data were used less frequently (9%). Tree-based methods (eg, random forests and boosting) were the most commonly used ML methods (31%). CONCLUSIONS The use of ML techniques in HEOR is growing rapidly, but there remain opportunities to apply them to predict economic outcomes, especially using claims databases, which could inform the development of cost-effectiveness models.
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Affiliation(s)
- Woojung Lee
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA.
| | - Naomi Schwartz
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Aasthaa Bansal
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Sara Khor
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Noah Hammarlund
- Department of Health Services Research, Management & Policy, University of Florida, Gainesville, FL, USA
| | - Anirban Basu
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Beth Devine
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
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Chen ZS, Kulkarni P(P, Galatzer-Levy IR, Bigio B, Nasca C, Zhang Y. Modern views of machine learning for precision psychiatry. PATTERNS (NEW YORK, N.Y.) 2022; 3:100602. [PMID: 36419447 PMCID: PMC9676543 DOI: 10.1016/j.patter.2022.100602] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.
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Affiliation(s)
- Zhe Sage Chen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY 11201, USA
| | | | - Isaac R. Galatzer-Levy
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Meta Reality Lab, New York, NY, USA
| | - Benedetta Bigio
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Carla Nasca
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, USA
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Rastpour A, McGregor C. Predicting Patient Wait Times by Using Highly Deidentified Data in Mental Health Care: Enhanced Machine Learning Approach. JMIR Ment Health 2022; 9:e38428. [PMID: 35943774 PMCID: PMC9399879 DOI: 10.2196/38428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 06/18/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Wait times impact patient satisfaction, treatment effectiveness, and the efficiency of care that the patients receive. Wait time prediction in mental health is a complex task and is affected by the difficulty in predicting the required number of treatment sessions for outpatients, high no-show rates, and the possibility of using group treatment sessions. The task of wait time analysis becomes even more challenging if the input data has low utility, which happens when the data is highly deidentified by removing both direct and quasi identifiers. OBJECTIVE The first aim of this study was to develop machine learning models to predict the wait time from referral to the first appointment for psychiatric outpatients by using real-time data. The second aim was to enhance the performance of these predictive models by utilizing the system's knowledge while the input data were highly deidentified. The third aim was to identify the factors that drove long wait times, and the fourth aim was to build these models such that they were practical and easy-to-implement (and therefore, attractive to care providers). METHODS We analyzed retrospective highly deidentified administrative data from 8 outpatient clinics at Ontario Shores Centre for Mental Health Sciences in Canada by using 6 machine learning methods to predict the first appointment wait time for new outpatients. We used the system's knowledge to mitigate the low utility of our data. The data included 4187 patients who received care through 30,342 appointments. RESULTS The average wait time varied widely between different types of mental health clinics. For more than half of the clinics, the average wait time was longer than 3 months. The number of scheduled appointments and the rate of no-shows varied widely among clinics. Despite these variations, the random forest method provided the minimum root mean square error values for 4 of the 8 clinics, and the second minimum root mean square error for the other 4 clinics. Utilizing the system's knowledge increased the utility of our highly deidentified data and improved the predictive power of the models. CONCLUSIONS The random forest method, enhanced with the system's knowledge, provided reliable wait time predictions for new outpatients, regardless of low utility of the highly deidentified input data and the high variation in wait times across different clinics and patient types. The priority system was identified as a factor that contributed to long wait times, and a fast-track system was suggested as a potential solution.
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Affiliation(s)
- Amir Rastpour
- Faculty of Business and Information Technology, Ontario Tech University, Oshawa, ON, Canada
| | - Carolyn McGregor
- Faculty of Business and Information Technology, Ontario Tech University, Oshawa, ON, Canada.,Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia
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Gopukumar D, Ghoshal A, Zhao H. A Machine Learning Approach for Predicting Readmission Charges Billed by Hospitals. JMIR Med Inform 2022; 10:e37578. [PMID: 35896038 PMCID: PMC9472041 DOI: 10.2196/37578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 05/02/2022] [Accepted: 07/26/2022] [Indexed: 11/29/2022] Open
Abstract
Background The Centers for Medicare and Medicaid Services projects that health care costs will continue to grow over the next few years. Rising readmission costs contribute significantly to increasing health care costs. Multiple areas of health care, including readmissions, have benefited from the application of various machine learning algorithms in several ways. Objective We aimed to identify suitable models for predicting readmission charges billed by hospitals. Our literature review revealed that this application of machine learning is underexplored. We used various predictive methods, ranging from glass-box models (such as regularization techniques) to black-box models (such as deep learning–based models). Methods We defined readmissions as readmission with the same major diagnostic category (RSDC) and all-cause readmission category (RADC). For these readmission categories, 576,701 and 1,091,580 individuals, respectively, were identified from the Nationwide Readmission Database of the Healthcare Cost and Utilization Project by the Agency for Healthcare Research and Quality for 2013. Linear regression, lasso regression, elastic net, ridge regression, eXtreme gradient boosting (XGBoost), and a deep learning model based on multilayer perceptron (MLP) were the 6 machine learning algorithms we tested for RSDC and RADC through 10-fold cross-validation. Results Our preliminary analysis using a data-driven approach revealed that within RADC, the subsequent readmission charge billed per patient was higher than the previous charge for 541,090 individuals, and this number was 319,233 for RSDC. The top 3 major diagnostic categories (MDCs) for such instances were the same for RADC and RSDC. The average readmission charge billed was higher than the previous charge for 21 of the MDCs in the case of RSDC, whereas it was only for 13 of the MDCs in RADC. We recommend XGBoost and the deep learning model based on MLP for predicting readmission charges. The following performance metrics were obtained for XGBoost: (1) RADC (mean absolute percentage error [MAPE]=3.121%; root mean squared error [RMSE]=0.414; mean absolute error [MAE]=0.317; root relative squared error [RRSE]=0.410; relative absolute error [RAE]=0.399; normalized RMSE [NRMSE]=0.040; mean absolute deviation [MAD]=0.031) and (2) RSDC (MAPE=3.171%; RMSE=0.421; MAE=0.321; RRSE=0.407; RAE=0.393; NRMSE=0.041; MAD=0.031). The performance obtained for MLP-based deep neural networks are as follows: (1) RADC (MAPE=3.103%; RMSE=0.413; MAE=0.316; RRSE=0.410; RAE=0.397; NRMSE=0.040; MAD=0.031) and (2) RSDC (MAPE=3.202%; RMSE=0.427; MAE=0.326; RRSE=0.413; RAE=0.399; NRMSE=0.041; MAD=0.032). Repeated measures ANOVA revealed that the mean RMSE differed significantly across models with P<.001. Post hoc tests using the Bonferroni correction method indicated that the mean RMSE of the deep learning/XGBoost models was statistically significantly (P<.001) lower than that of all other models, namely linear regression/elastic net/lasso/ridge regression. Conclusions Models built using XGBoost and MLP are suitable for predicting readmission charges billed by hospitals. The MDCs allow models to accurately predict hospital readmission charges.
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Affiliation(s)
- Deepika Gopukumar
- Department of Health and Clinical Outcomes Research, School of Medicine, Saint Louis University, SALUS Center, 3545 Lafayette Ave., 4rth floor, Room 409 B, St.Louis, US
| | - Abhijeet Ghoshal
- Department of Business Administration, Gies College of Business, University of Illinois Urbana-Champaign, Champaign, US
| | - Huimin Zhao
- Sheldon B. Lubar College of Business, University of Wisconsin-Milwaukee, Milwaukee, US
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Roberts W, Zhao Y, Verplaetse T, Moore KE, Peltier MR, Burke C, Zakiniaeiz Y, McKee S. Using machine learning to predict heavy drinking during outpatient alcohol treatment. Alcohol Clin Exp Res 2022; 46:657-666. [PMID: 35420710 PMCID: PMC9180421 DOI: 10.1111/acer.14802] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 02/15/2022] [Accepted: 02/22/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Accurate clinical prediction supports the effective treatment of alcohol use disorder (AUD) and other psychiatric disorders. Traditional statistical techniques have identified patient characteristics associated with treatment outcomes. However, less work has focused on systematically leveraging these associations to create optimal predictive models. The current study demonstrates how machine learning can be used to predict clinical outcomes in people completing outpatient AUD treatment. METHOD We used data from the COMBINE multisite clinical trial (n = 1383) to develop and test predictive models. We identified three priority prediction targets, including (1) heavy drinking during the first month of treatment, (2) heavy drinking during the last month of treatment, and (3) heavy drinking between weekly/bi-weekly sessions. Models were generated using the random forest algorithm. We used "leave sites out" partitioning to externally validate the models in trial sites that were not included in the model training. Stratified model development was used to test for sex differences in the relative importance of predictive features. RESULTS Models predicting heavy alcohol use during the first and last months of treatment showed internal cross-validation area under the curve (AUC) scores ranging from 0.67 to 0.74. AUC was comparable in the external validation using data from held-out sites (AUC range = 0.69 to 0.72). The model predicting between-session heavy drinking showed strong classification accuracy in internal cross-validation (AUC = 0.89) and external test samples (AUC range = 0.80 to 0.87). Stratified analyses showed substantial sex differences in optimal feature sets. CONCLUSION Machine learning techniques can predict alcohol treatment outcomes using routinely collected clinical data. This technique has the potential to greatly improve clinical prediction accuracy without requiring expensive or invasive assessment methods. More research is needed to understand how best to deploy these models.
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Affiliation(s)
- Walter Roberts
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA.,Department of Psychology, East Tennessee State University, Johnson City, Tennessee, USA
| | - Yize Zhao
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Terril Verplaetse
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Kelly E Moore
- Department of Psychology, East Tennessee State University, Johnson City, Tennessee, USA
| | - MacKenzie R Peltier
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA.,Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, USA
| | - Catherine Burke
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Yasmin Zakiniaeiz
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Sherry McKee
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
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Yasar O, Long P, Harder B, Marshall H, Bhasin S, Lee S, Delegge M, Roy S, Doyle O, Leavitt N, Rigg J. Machine learning using longitudinal prescription and medical claims for the detection of non-alcoholic steatohepatitis (NASH). BMJ Health Care Inform 2022; 29:bmjhci-2021-100510. [PMID: 35354641 PMCID: PMC8968511 DOI: 10.1136/bmjhci-2021-100510] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 03/13/2022] [Indexed: 12/26/2022] Open
Abstract
Objectives To develop and evaluate machine learning models to detect patients with suspected undiagnosed non-alcoholic steatohepatitis (NASH) for diagnostic screening and clinical management. Methods In this retrospective observational non-interventional study using administrative medical claims data from 1 463 089 patients, gradient-boosted decision trees were trained to detect patients with likely NASH from an at-risk patient population with a history of obesity, type 2 diabetes mellitus, metabolic disorder or non-alcoholic fatty liver (NAFL). Models were trained to detect likely NASH in all at-risk patients or in the subset without a prior NAFL diagnosis (at-risk non-NAFL patients). Models were trained and validated using retrospective medical claims data and assessed using area under precision recall curves and receiver operating characteristic curves (AUPRCs and AUROCs). Results The 6-month incidences of NASH in claims data were 1 per 1437 at-risk patients and 1 per 2127 at-risk non-NAFL patients. The model trained to detect NASH in all at-risk patients had an AUPRC of 0.0107 (95% CI 0.0104 to 0.0110) and an AUROC of 0.84. At 10% recall, model precision was 4.3%, which is 60× above NASH incidence. The model trained to detect NASH in the non-NAFL cohort had an AUPRC of 0.0030 (95% CI 0.0029 to 0.0031) and an AUROC of 0.78. At 10% recall, model precision was 1%, which is 20× above NASH incidence. Conclusion The low incidence of NASH in medical claims data corroborates the pattern of NASH underdiagnosis in clinical practice. Claims-based machine learning could facilitate the detection of patients with probable NASH for diagnostic testing and disease management.
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Affiliation(s)
| | - Patrick Long
- Real World Solutions, IQVIA, Plymouth Meeting, Pennsylvania, USA
| | - Brett Harder
- Real World Solutions, IQVIA, Plymouth Meeting, Pennsylvania, USA
| | - Hanna Marshall
- Real World Solutions, IQVIA, Plymouth Meeting, Pennsylvania, USA
| | - Sanjay Bhasin
- Real World Solutions, IQVIA, Plymouth Meeting, Pennsylvania, USA
| | - Suyin Lee
- Real World Solutions, IQVIA, Plymouth Meeting, Pennsylvania, USA
| | - Mark Delegge
- Therapeutic Center of Excellence, IQVIA, Durham, North Carolina, USA
| | - Stephanie Roy
- Real World Solutions, IQVIA, Plymouth Meeting, Pennsylvania, USA
| | | | - Nadea Leavitt
- Real World Solutions, IQVIA, Plymouth Meeting, Pennsylvania, USA
| | - John Rigg
- Real World Solutions, IQVIA, London, UK
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11
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Chen Y. Prediction and Analysis of Length of Stay Based on Nonlinear Weighted XGBoost Algorithm in Hospital. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:4714898. [PMID: 34900191 PMCID: PMC8654524 DOI: 10.1155/2021/4714898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 11/19/2021] [Accepted: 11/20/2021] [Indexed: 11/18/2022]
Abstract
An improved nonlinear weighted extreme gradient boosting (XGBoost) technique is developed to forecast length of stay for patients with imbalance data. The algorithm first chooses an effective technique for fitting the duration of stay and determining the distribution law and then optimizes the negative log likelihood loss function using a heuristic nonlinear weighting method based on sample percentage. Theoretical and practical results reveal that, when compared to existing algorithms, the XGBoost method based on nonlinear weighting may achieve higher classification accuracy and better prediction performance, which is beneficial in treating more patients with fewer hospital beds.
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Affiliation(s)
- Yong Chen
- The Affiliated Hospital of Chengde Medical College, Chengde, Hebei 067000, China
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12
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The Promise for Reducing Healthcare Cost with Predictive Model: An Analysis with Quantized Evaluation Metric on Readmission. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:9208138. [PMID: 34765104 PMCID: PMC8577942 DOI: 10.1155/2021/9208138] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 10/15/2021] [Indexed: 11/23/2022]
Abstract
Quality of care data has gained transparency captured through various measurements and reporting. Readmission measure is especially related to unfavorable patient outcomes that directly bends the curve of healthcare cost. Under the Hospital Readmission Reduction Program, payments to hospitals were reduced for those with excessive 30-day rehospitalization rates. These penalties have intensified efforts from hospital stakeholders to implement strategies to reduce readmission rates. One of the key strategies is the deployment of predictive analytics stratified by patient population. The recent research in readmission model is focused on making its prediction more accurate. As cost-saving improvements through artificial intelligent-based health solutions are expected, the broad economic impact of such digital tool remains unknown. Meanwhile, reducing readmission rate is associated with increased operating expenses due to targeted interventions. The increase in operating margin can surpass native readmission cost. In this paper, we propose a quantized evaluation metric to provide a methodological mean in assessing whether a predictive model represents cost-effective way of delivering healthcare. Herein, we evaluate the impact machine learning has had on transitional care and readmission with proposed metric. The final model was estimated to produce net healthcare savings at over $1 million given a 50% rate of successfully preventing a readmission.
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13
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Qian C, Leelaprachakul P, Landers M, Low C, Dey AK, Doryab A. Prediction of Hospital Readmission from Longitudinal Mobile Data Streams. SENSORS 2021; 21:s21227510. [PMID: 34833586 PMCID: PMC8618459 DOI: 10.3390/s21227510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 11/03/2021] [Accepted: 11/08/2021] [Indexed: 11/16/2022]
Abstract
Hospital readmissions impose an extreme burden on both health systems and patients. Timely management of the postoperative complications that result in readmissions is necessary to mitigate the effects of these events. However, accurately predicting readmissions is very challenging, and current approaches demonstrated a limited ability to forecast which patients are likely to be readmitted. Our research addresses the challenge of daily readmission risk prediction after the hospital discharge via leveraging the abilities of mobile data streams collected from patients devices in a probabilistic deep learning framework. Through extensive experiments on a real-world dataset that includes smartphone and Fitbit device data from 49 patients collected for 60 days after discharge, we demonstrate our framework's ability to closely simulate the readmission risk trajectories for cancer patients.
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Affiliation(s)
- Chen Qian
- Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22904, USA;
| | - Patraporn Leelaprachakul
- Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213, USA;
| | - Matthew Landers
- Department of Computer Science, University of Virginia, Charlottesville, VA 22904, USA;
| | - Carissa Low
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA;
| | - Anind K. Dey
- Information School, University of Washington, Seattle, WA 98105, USA;
| | - Afsaneh Doryab
- Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22904, USA;
- Department of Computer Science, University of Virginia, Charlottesville, VA 22904, USA;
- Correspondence:
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14
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Yang L, Yang X, Yang T, Wu X, Sun P, Zhu Y, Su Y, Gu W, Qiu H, Wang J, Chen J, Fang Y. The effect of thyroid function on the risk of psychiatric readmission after hospitalization for major depressive disorder. Psychiatry Res 2021; 305:114205. [PMID: 34551381 DOI: 10.1016/j.psychres.2021.114205] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 08/28/2021] [Accepted: 09/04/2021] [Indexed: 11/18/2022]
Abstract
The relationship between thyroid function and depression has long been recognized, but little is known about the effect of thyroid function on the risk of readmission after hospitalization for major depressive disorder (MDD). This retrospective cohort study was aimed to explore the effect of thyroid function on psychiatric readmission after hospitalization for MDD. Data was derived from electronic medical records (EMR) of the Shanghai Mental Health Center (SMHC), Shanghai, China. Univariate and multivariate logistic regression analyses were conducted in subjects aged ≥ 18 years who had been hospitalized for MDD between January 1, 2007, and May 31, 2019. Of the 1803 eligible patients, 85 and 132 patients experienced psychiatric readmission within 90 days and 180 days after discharge respectively. Multivariate analyses showed that serum FT3 level (aOR=1.271; 95%CI=1.051-1.537) and comorbidity of thyroid disease (aOR=2,179; 95%CI=1.136-4.179) was independently associated with the risk of 90-day and 180-day readmission respectively. These findings indicated that high serum FT3 levels and comorbidity of thyroid disease could increase the risk of readmission after hospitalization for MDD. It is warranted to provide routine assessment and intervention of the thyroid function during the treatment of depression so as to prevent re-hospitalization.
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Affiliation(s)
- Lu Yang
- Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Xiaorui Yang
- Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Tao Yang
- Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Xiaohui Wu
- Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Ping Sun
- Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Yuncheng Zhu
- Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Yousong Su
- Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Wenjie Gu
- Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Hong Qiu
- Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Jianli Wang
- School of Epidemiology and Public Health, Department of Psychiatry, Faculty of Medicine & Institute of Mental Health Research, University of Ottawa, Ontario K1N 6N5, Canada
| | - Jun Chen
- Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai 200031, China; Shanghai Key Laboratory of Psychotic Disorders, Shanghai 201108, China; Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Guangzhou 510515, China.
| | - Yiru Fang
- Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai 200031, China; Shanghai Key Laboratory of Psychotic Disorders, Shanghai 201108, China; Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Guangzhou 510515, China.
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15
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Abstract
Health is often qualitatively defined as a status free from disease and its quantitative definition requires finding the boundary separating health from pathological conditions. Since many complex diseases have a strong genetic component, substantial efforts have been made to sequence large-scale personal genomes; however, we are not yet able to effectively quantify health status from personal genomes. Since mutational impacts are ultimately manifested at the protein level, we envision that introducing a panoramic proteomic view of complex diseases will allow us to mechanistically understand the molecular etiologies of human diseases. In this perspective article, we will highlight key proteomic approaches to identify pathogenic mutations and map their convergent pathways underlying disease pathogenesis and the integration of omics data at multiple levels to define the borderline between health and disease.
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Affiliation(s)
- Mara Zilocchi
- Department of Biochemistry, University of Regina, Regina, Saskatchewan S4S 0A2, Canada
| | - Cheng Wang
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, the Bakar Computational Health Sciences Institute, the Parker Institute for Cancer Immunotherapy, and the Department of Neurology, School of Medicine, University of California, San Francisco, CA, USA
| | - Mohan Babu
- Department of Biochemistry, University of Regina, Regina, Saskatchewan S4S 0A2, Canada
| | - Jingjing Li
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, the Bakar Computational Health Sciences Institute, the Parker Institute for Cancer Immunotherapy, and the Department of Neurology, School of Medicine, University of California, San Francisco, CA, USA
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16
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Teo K, Yong CW, Chuah JH, Hum YC, Tee YK, Xia K, Lai KW. Current Trends in Readmission Prediction: An Overview of Approaches. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021; 48:1-18. [PMID: 34422543 PMCID: PMC8366485 DOI: 10.1007/s13369-021-06040-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 07/30/2021] [Indexed: 12/03/2022]
Abstract
Hospital readmission shortly after discharge threatens the quality of patient care and leads to increased medical care costs. In the United States, hospitals with high readmission rates are subject to federal financial penalties. This concern calls for incentives for healthcare facilities to reduce their readmission rates by predicting patients who are at high risk of readmission. Conventional practices involve the use of rule-based assessment scores and traditional statistical methods, such as logistic regression, in developing risk prediction models. The recent advancements in machine learning driven by improved computing power and sophisticated algorithms have the potential to produce highly accurate predictions. However, the value of such models could be overrated. Meanwhile, the use of other flexible models that leverage simple algorithms offer great transparency in terms of feature interpretation, which is beneficial in clinical settings. This work presents an overview of the current trends in risk prediction models developed in the field of readmission. The various techniques adopted by researchers in recent years are described, and the topic of whether complex models outperform simple ones in readmission risk stratification is investigated.
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Affiliation(s)
- Kareen Teo
- Department of Biomedical Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Ching Wai Yong
- Department of Biomedical Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Joon Huang Chuah
- Department of Electrical Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Yan Chai Hum
- Department of Mechatronics and Biomedical Engineering, Universiti Tunku Abdul Rahman, 43000 Sungai Long, Malaysia
| | - Yee Kai Tee
- Department of Mechatronics and Biomedical Engineering, Universiti Tunku Abdul Rahman, 43000 Sungai Long, Malaysia
| | - Kaijian Xia
- Changshu Institute of Technology, Changshu, 215500 Jiangsu China
| | - Khin Wee Lai
- Department of Biomedical Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
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17
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Delanerolle G, Yang X, Shetty S, Raymont V, Shetty A, Phiri P, Hapangama DK, Tempest N, Majumder K, Shi JQ. Artificial intelligence: A rapid case for advancement in the personalization of Gynaecology/Obstetric and Mental Health care. ACTA ACUST UNITED AC 2021; 17:17455065211018111. [PMID: 33990172 PMCID: PMC8127586 DOI: 10.1177/17455065211018111] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
To evaluate and holistically treat the mental health sequelae and potential psychiatric comorbidities associated with obstetric and gynaecological conditions, it is important to optimize patient care, ensure efficient use of limited resources and improve health-economic models. Artificial intelligence applications could assist in achieving the above. The World Health Organization and global healthcare systems have already recognized the use of artificial intelligence technologies to address 'system gaps' and automate some of the more cumbersome tasks to optimize clinical services and reduce health inequalities. Currently, both mental health and obstetric and gynaecological services independently use artificial intelligence applications. Thus, suitable solutions are shared between mental health and obstetric and gynaecological clinical practices, independent of one another. Although, to address complexities with some patients who may have often interchanging sequelae with mental health and obstetric and gynaecological illnesses, 'holistically' developed artificial intelligence applications could be useful. Therefore, we present a rapid review to understand the currently available artificial intelligence applications and research into multi-morbid conditions, including clinical trial-based validations. Most artificial intelligence applications are intrinsically data-driven tools, and their validation in healthcare can be challenging as they require large-scale clinical trials. Furthermore, most artificial intelligence applications use rate-limiting mock data sets, which restrict their applicability to a clinical population. Some researchers may fail to recognize the randomness in the data generating processes in clinical care from a statistical perspective with a potentially minimal representation of a population, limiting their applicability within a real-world setting. However, novel, innovative trial designs could pave the way to generate better data sets that are generalizable to the entire global population. A collaboration between artificial intelligence and statistical models could be developed and deployed with algorithmic and domain interpretability to achieve this. In addition, acquiring big data sets is vital to ensure these artificial intelligence applications provide the highest accuracy within a real-world setting, especially when used as part of a clinical diagnosis or treatment.
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Affiliation(s)
| | - Xuzhi Yang
- Southern University of Science and Technology, Shenzhen, China
| | | | | | - Ashish Shetty
- University College London, London, UK.,University College London NHS Foundation Trust, London, UK
| | - Peter Phiri
- Southern Health NHS Foundation Trust, Southampton, UK.,Primary Care, Population Sciences and Medical Education, University of Southampton, Southampton, UK
| | | | | | - Kingshuk Majumder
- University of Manchester Hospitals NHS Foundation Trust, Manchester, UK
| | - Jian Qing Shi
- Southern University of Science and Technology, Shenzhen, China.,The Alan Turing Institute, London, UK
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18
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Understanding current states of machine learning approaches in medical informatics: a systematic literature review. HEALTH AND TECHNOLOGY 2021. [DOI: 10.1007/s12553-021-00538-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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19
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Elujide I, Fashoto SG, Fashoto B, Mbunge E, Folorunso SO, Olamijuwon JO. Application of deep and machine learning techniques for multi-label classification performance on psychotic disorder diseases. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100545] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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20
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Zhang Z, Qiu H, Li W, Chen Y. A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction. BMC Med Inform Decis Mak 2020; 20:335. [PMID: 33317534 PMCID: PMC7734833 DOI: 10.1186/s12911-020-01358-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 11/30/2020] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Acute myocardial infarction (AMI) is a serious cardiovascular disease, followed by a high readmission rate within 30-days of discharge. Accurate prediction of AMI readmission is a crucial way to identify the high-risk group and optimize the distribution of medical resources. METHODS In this study, we propose a stacking-based model to predict the risk of 30-day unplanned all-cause hospital readmissions for AMI patients based on clinical data. Firstly, we conducted an under-sampling method of neighborhood cleaning rule (NCR) to alleviate the class imbalance and then utilized a feature selection method of SelectFromModel (SFM) to select effective features. Secondly, we adopted a self-adaptive approach to select base classifiers from eight candidate models according to their performances in datasets. Finally, we constructed a three-layer stacking model in which layer 1 and layer 2 were base-layer and level 3 was meta-layer. The predictions of the base-layer were used to train the meta-layer in order to make the final forecast. RESULTS The results show that the proposed model exhibits the highest AUC (0.720), which is higher than that of decision tree (0.681), support vector machine (0.707), random forest (0.701), extra trees (0.709), adaBoost (0.702), bootstrap aggregating (0.704), gradient boosting decision tree (0.710) and extreme gradient enhancement (0.713). CONCLUSION It is evident that our model could effectively predict the risk of 30-day all cause hospital readmissions for AMI patients and provide decision support for the administration.
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Affiliation(s)
- Zhen Zhang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731, Chengdu, Sichuan, PR China.,Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China
| | - Hang Qiu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731, Chengdu, Sichuan, PR China. .,Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China.
| | - Weihao Li
- Cardiology Division, West China Hospital, Sichuan University, No.17 People's South Road,Chengdu, 610041, Chengdu, Sichuan, PR China.,West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yucheng Chen
- Cardiology Division, West China Hospital, Sichuan University, No.17 People's South Road,Chengdu, 610041, Chengdu, Sichuan, PR China. .,West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
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21
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Wollenstein-Betech S, Cassandras CG, Paschalidis IC. Personalized predictive models for symptomatic COVID-19 patients using basic preconditions: Hospitalizations, mortality, and the need for an ICU or ventilator. Int J Med Inform 2020; 142:104258. [PMID: 32927229 PMCID: PMC7442577 DOI: 10.1016/j.ijmedinf.2020.104258] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Revised: 07/26/2020] [Accepted: 08/17/2020] [Indexed: 01/08/2023]
Abstract
BACKGROUND The rapid global spread of the SARS-CoV-2 virus has provoked a spike in demand for hospital care. Hospital systems across the world have been over-extended, including in Northern Italy, Ecuador, and New York City, and many other systems face similar challenges. As a result, decisions on how to best allocate very limited medical resources and design targeted policies for vulnerable subgroups have come to the forefront. Specifically, under consideration are decisions on who to test, who to admit into hospitals, who to treat in an Intensive Care Unit (ICU), and who to support with a ventilator. Given today's ability to gather, share, analyze and process data, personalized predictive models based on demographics and information regarding prior conditions can be used to (1) help decision-makers allocate limited resources, when needed, (2) advise individuals how to better protect themselves given their risk profile, (3) differentiate social distancing guidelines based on risk, and (4) prioritize vaccinations once a vaccine becomes available. OBJECTIVE To develop personalized models that predict the following events: (1) hospitalization, (2) mortality, (3) need for ICU, and (4) need for a ventilator. To predict hospitalization, it is assumed that one has access to a patient's basic preconditions, which can be easily gathered without the need to be at a hospital and hence serve citizens and policy makers to assess individual risk during a pandemic. For the remaining models, different versions developed include different sets of a patient's features, with some including information on how the disease is progressing (e.g., diagnosis of pneumonia). MATERIALS AND METHODS National data from a publicly available repository, updated daily, containing information from approximately 91,000 patients in Mexico were used. The data for each patient include demographics, prior medical conditions, SARS-CoV-2 test results, hospitalization, mortality and whether a patient has developed pneumonia or not. Several classification methods were applied and compared, including robust versions of logistic regression, and support vector machines, as well as random forests and gradient boosted decision trees. RESULTS Interpretable methods (logistic regression and support vector machines) perform just as well as more complex models in terms of accuracy and detection rates, with the additional benefit of elucidating variables on which the predictions are based. Classification accuracies reached 72 %, 79 %, 89 %, and 90 % for predicting hospitalization, mortality, need for ICU and need for a ventilator, respectively. The analysis reveals the most important preconditions for making the predictions. For the four models derived, these are: (1) for hospitalization:age, pregnancy, diabetes, gender, chronic renal insufficiency, and immunosuppression; (2) for mortality: age, immunosuppression, chronic renal insufficiency, obesity and diabetes; (3) for ICU need: development of pneumonia (if available), age, obesity, diabetes and hypertension; and (4) for ventilator need: ICU and pneumonia (if available), age, obesity, and hypertension.
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Affiliation(s)
- Salomón Wollenstein-Betech
- Department of Electrical & Computer Engineering, Division of Systems Engineering, Boston University, 8 Saint Mary's St., Boston, MA 02215, USA
| | - Christos G Cassandras
- Department of Electrical & Computer Engineering, Division of Systems Engineering, Boston University, 8 Saint Mary's St., Boston, MA 02215, USA
| | - Ioannis Ch Paschalidis
- Department of Electrical & Computer Engineering, Division of Systems Engineering, Boston University, 8 Saint Mary's St., Boston, MA 02215, USA; Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, MA 02215, USA.
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22
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Wollenstein-Betech S, Cassandras CG, Paschalidis IC. Personalized Predictive Models for Symptomatic COVID-19 Patients Using Basic Preconditions: Hospitalizations, Mortality, and the Need for an ICU or Ventilator. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.05.03.20089813. [PMID: 32511489 PMCID: PMC7273257 DOI: 10.1101/2020.05.03.20089813] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND The rapid global spread of the virus SARS-CoV-2 has provoked a spike in demand for hospital care. Hospital systems across the world have been over-extended, including in Northern Italy, Ecuador, and New York City, and many other systems face similar challenges. As a result, decisions on how to best allocate very limited medical resources have come to the forefront. Specifically, under consideration are decisions on who to test, who to admit into hospitals, who to treat in an Intensive Care Unit (ICU), and who to support with a ventilator. Given today's ability to gather, share, analyze and process data, personalized predictive models based on demographics and information regarding prior conditions can be used to (1) help decision-makers allocate limited resources, when needed, (2) advise individuals how to better protect themselves given their risk profile, (3) differentiate social distancing guidelines based on risk, and (4) prioritize vaccinations once a vaccine becomes available. OBJECTIVE To develop personalized models that predict the following events: (1) hospitalization, (2) mortality, (3) need for ICU, and (4) need for a ventilator. To predict hospitalization, it is assumed that one has access to a patient's basic preconditions, which can be easily gathered without the need to be at a hospital. For the remaining models, different versions developed include different sets of a patient's features, with some including information on how the disease is progressing (e.g., diagnosis of pneumonia). MATERIALS AND METHODS Data from a publicly available repository, updated daily, containing information from approximately 91,000 patients in Mexico were used. The data for each patient include demographics, prior medical conditions, SARS-CoV-2 test results, hospitalization, mortality and whether a patient has developed pneumonia or not. Several classification methods were applied, including robust versions of logistic regression, and support vector machines, as well as random forests and gradient boosted decision trees. RESULTS Interpretable methods (logistic regression and support vector machines) perform just as well as more complex models in terms of accuracy and detection rates, with the additional benefit of elucidating variables on which the predictions are based. Classification accuracies reached 61%, 76%, 83%, and 84% for predicting hospitalization, mortality, need for ICU and need for a ventilator, respectively. The analysis reveals the most important preconditions for making the predictions. For the four models derived, these are: (1) for hospitalization: age, gender, chronic renal insufficiency, diabetes, immunosuppression; (2) for mortality: age, SARS-CoV-2 test status, immunosuppression and pregnancy; (3) for ICU need: development of pneumonia (if available), cardiovascular disease, asthma, and SARS-CoV-2 test status; and (4) for ventilator need: ICU and pneumonia (if available), age, gender, cardiovascular disease, obesity, pregnancy, and SARS-CoV-2 test result.
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Affiliation(s)
- Salomón Wollenstein-Betech
- Division of Systems Engineering, Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215
| | - Christos G Cassandras
- Division of Systems Engineering, Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215
| | - Ioannis Ch Paschalidis
- Division of Systems Engineering, Department of Electrical and Computer Engineering, Department of Biomedical Engineering, Boston University, Boston, MA 02215
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