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Caruana A, Bandara M, Musial K, Catchpoole D, Kennedy PJ. Machine learning for administrative health records: A systematic review of techniques and applications. Artif Intell Med 2023; 144:102642. [PMID: 37783537 DOI: 10.1016/j.artmed.2023.102642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 08/21/2023] [Accepted: 08/25/2023] [Indexed: 10/04/2023]
Abstract
Machine learning provides many powerful and effective techniques for analysing heterogeneous electronic health records (EHR). Administrative Health Records (AHR) are a subset of EHR collected for administrative purposes, and the use of machine learning on AHRs is a growing subfield of EHR analytics. Existing reviews of EHR analytics emphasise that the data-modality of the EHR limits the breadth of suitable machine learning techniques, and pursuable healthcare applications. Despite emphasising the importance of data modality, the literature fails to analyse which techniques and applications are relevant to AHRs. AHRs contain uniquely well-structured, categorically encoded records which are distinct from other data-modalities captured by EHRs, and they can provide valuable information pertaining to how patients interact with the healthcare system. This paper systematically reviews AHR-based research, analysing 70 relevant studies and spanning multiple databases. We identify and analyse which machine learning techniques are applied to AHRs and which health informatics applications are pursued in AHR-based research. We also analyse how these techniques are applied in pursuit of each application, and identify the limitations of these approaches. We find that while AHR-based studies are disconnected from each other, the use of AHRs in health informatics research is substantial and accelerating. Our synthesis of these studies highlights the utility of AHRs for pursuing increasingly complex and diverse research objectives despite a number of pervading data- and technique-based limitations. Finally, through our findings, we propose a set of future research directions that can enhance the utility of AHR data and machine learning techniques for health informatics research.
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Affiliation(s)
- Adrian Caruana
- Australian Artificial Intelligence Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia.
| | - Madhushi Bandara
- Australian Artificial Intelligence Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia
| | - Katarzyna Musial
- Complex Adaptive Systems Lab, Data Science Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia
| | - Daniel Catchpoole
- Australian Artificial Intelligence Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia; Biospecimen Research Services, The Children's Cancer Research Unit, The Children's Hospital at Westmead, Australia
| | - Paul J Kennedy
- Australian Artificial Intelligence Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia; Joint Research Centre in AI for Health and Wellness, University of Technology Sydney, Australia, and Ontario Tech University, Canada
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2
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Hong JC, Hauser ER, Redding TS, Sims KJ, Gellad ZF, O'Leary MC, Hyslop T, Madison AN, Qin X, Weiss D, Bullard AJ, Williams CD, Sullivan BA, Lieberman D, Provenzale D. Characterizing chronological accumulation of comorbidities in healthy veterans: a computational approach. Sci Rep 2021; 11:8104. [PMID: 33854078 PMCID: PMC8046765 DOI: 10.1038/s41598-021-85546-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 12/14/2020] [Indexed: 12/13/2022] Open
Abstract
Understanding patient accumulation of comorbidities can facilitate healthcare strategy and personalized preventative care. We applied a directed network graph to electronic health record (EHR) data and characterized comorbidities in a cohort of healthy veterans undergoing screening colonoscopy. The Veterans Affairs Cooperative Studies Program #380 was a prospective longitudinal study of screening and surveillance colonoscopy. We identified initial instances of three-digit ICD-9 diagnoses for participants with at least 5 years of linked EHR history (October 1999 to December 2015). For diagnoses affecting at least 10% of patients, we calculated pairwise chronological relative risk (RR). iGraph was used to produce directed graphs of comorbidities with RR > 1, as well as summary statistics, key diseases, and communities. A directed graph based on 2210 patients visualized longitudinal development of comorbidities. Top hub (preceding) diseases included ischemic heart disease, inflammatory and toxic neuropathy, and diabetes. Top authority (subsequent) diagnoses were acute kidney failure and hypertensive chronic kidney failure. Four communities of correlated comorbidities were identified. Close analysis of top hub and authority diagnoses demonstrated known relationships, correlated sequelae, and novel hypotheses. Directed network graphs portray chronologic comorbidity relationships. We identified relationships between comorbid diagnoses in this aging veteran cohort. This may direct healthcare prioritization and personalized care.
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Affiliation(s)
- Julian C Hong
- Cooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care System, Durham, NC, USA. .,Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA. .,Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
| | - Elizabeth R Hauser
- Cooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care System, Durham, NC, USA.,Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Thomas S Redding
- Cooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care System, Durham, NC, USA
| | - Kellie J Sims
- Cooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care System, Durham, NC, USA
| | - Ziad F Gellad
- Cooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care System, Durham, NC, USA.,Department of Medicine, Duke University, Durham, NC, USA
| | - Meghan C O'Leary
- Cooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care System, Durham, NC, USA
| | - Terry Hyslop
- Cooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care System, Durham, NC, USA.,Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Ashton N Madison
- Cooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care System, Durham, NC, USA
| | - Xuejun Qin
- Cooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care System, Durham, NC, USA.,Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - David Weiss
- Cooperative Studies Program Coordinating Center, Perry Point VA Medical Center, Perry Point, MD, USA
| | - A Jasmine Bullard
- Cooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care System, Durham, NC, USA
| | - Christina D Williams
- Cooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care System, Durham, NC, USA.,Department of Medicine, Duke University, Durham, NC, USA
| | - Brian A Sullivan
- Cooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care System, Durham, NC, USA.,Department of Medicine, Duke University, Durham, NC, USA
| | - David Lieberman
- VA Portland Health Care System, Portland, OR, USA.,Oregon Health and Science University, Portland, OR, USA
| | - Dawn Provenzale
- Cooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care System, Durham, NC, USA. .,Department of Medicine, Duke University, Durham, NC, USA.
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3
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Haug N, Sorger J, Gisinger T, Gyimesi M, Kautzky-Willer A, Thurner S, Klimek P. Decompression of Multimorbidity Along the Disease Trajectories of Diabetes Mellitus Patients. Front Physiol 2021; 11:612604. [PMID: 33469431 PMCID: PMC7813935 DOI: 10.3389/fphys.2020.612604] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 12/04/2020] [Indexed: 11/13/2022] Open
Abstract
Multimorbidity, the presence of two or more diseases in a patient, is maybe the greatest health challenge for the aging populations of many high-income countries. One of the main drivers of multimorbidity is diabetes mellitus (DM) due to its large number of risk factors and complications. Yet, we currently have very limited understanding of how to quantify multimorbidity beyond a simple counting of diseases and thereby inform prevention and intervention strategies tailored to the needs of elderly DM patients. Here, we conceptualize multimorbidity as typical temporal progression patterns of multiple diseases, so-called trajectories, and develop a framework to perform a matched and sex-specific comparison between DM and non-diabetic patients. We find that these disease trajectories can be organized into a multi-level hierarchy in which DM patients progress from relatively healthy states with low mortality to high-mortality states characterized by cardiovascular diseases, chronic lower respiratory diseases, renal failure, and different combinations thereof. The same disease trajectories can be observed in non-diabetic patients, however, we find that DM patients typically progress at much higher rates along their trajectories. Comparing male and female DM patients, we find a general tendency that females progress faster toward high multimorbidity states than males, in particular along trajectories that involve obesity. Males, on the other hand, appear to progress faster in trajectories that combine heart diseases with cerebrovascular diseases. Our results show that prevention and efficient management of DM are key to achieve a compression of morbidity into higher patient ages. Multidisciplinary efforts involving clinicians as well as experts in machine learning and data visualization are needed to better understand the identified disease trajectories and thereby contribute to solving the current multimorbidity crisis in healthcare.
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Affiliation(s)
- Nils Haug
- Section for Science of Complex Systems, CeMSIIS, Medical University of Vienna, Vienna, Austria.,Complexity Science Hub Vienna, Vienna, Austria
| | | | - Teresa Gisinger
- Department of Medicine III, Endocrinology and Metabolism, Medical University of Vienna, Vienna, Austria
| | | | - Alexandra Kautzky-Willer
- Department of Medicine III, Endocrinology and Metabolism, Medical University of Vienna, Vienna, Austria.,Gender Institute, Gars am Kamp, Austria
| | - Stefan Thurner
- Section for Science of Complex Systems, CeMSIIS, Medical University of Vienna, Vienna, Austria.,Complexity Science Hub Vienna, Vienna, Austria.,IIASA, Laxenburg, Austria.,Santa Fe Institute, Santa Fe, NM, United States
| | - Peter Klimek
- Section for Science of Complex Systems, CeMSIIS, Medical University of Vienna, Vienna, Austria.,Complexity Science Hub Vienna, Vienna, Austria
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4
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Kaleta M, Niederkrotenthaler T, Kautzky-Willer A, Klimek P. How Specialist Aftercare Impacts Long-Term Readmission Risks in Elderly Patients With Metabolic, Cardiac, and Chronic Obstructive Pulmonary Diseases: Cohort Study Using Administrative Data. JMIR Med Inform 2020; 8:e18147. [PMID: 32936077 PMCID: PMC7527915 DOI: 10.2196/18147] [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: 02/06/2020] [Revised: 06/26/2020] [Accepted: 06/28/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The health state of elderly patients is typically characterized by multiple co-occurring diseases requiring the involvement of several types of health care providers. OBJECTIVE We aimed to quantify the benefit for multimorbid patients from seeking specialist care in terms of long-term readmission risks. METHODS From an administrative database, we identified 225,238 elderly patients with 97 different diagnosis (ICD-10 codes) from hospital stays and contact with 13 medical specialties. For each diagnosis associated with the first hospital stay, we used multiple logistic regression analysis to quantify the sex-specific and age-adjusted long-term all-cause readmission risk (hospitalizations occurring between 3 months and 3 years after the first admission) and how specialist contact impacts these risks. RESULTS Men have a higher readmission risk than women (mean difference over all first diagnoses 1.9%, P<.001), but similar reduction in readmission risk after receiving specialist care. Specialist care can reduce readmission risk by almost 50%. We found the greatest reductions in risk when the first hospital stay was associated with diagnoses corresponding to complex chronic diseases such as acute myocardial infarction (57.6% reduction in readmission risk, SE 7.6% for men [m]; 55.9% reduction, SE 9.8% for women [w]), diabetic and other retinopathies (m: 62.3%, SE 8.0; w: 60.1%, SE 8.4%), chronic obstructive pulmonary disease (m: 63.9%, SE 7.8%; w: 58.1%, SE 7.5%), disorders of lipoprotein metabolism (m: 64.7%, SE 3.7%; w: 63.8%, SE 4.0%), and chronic ischemic heart diseases (m: 63.6%, SE 3.1%; w: 65.4%, SE 3.0%). CONCLUSIONS Specialist care can greatly reduce long-term readmission risk for patients with chronic and multimorbid diseases. Further research is needed to identify the specific reasons for these findings and to understand the detected sex-specific differences.
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Affiliation(s)
- Michaela Kaleta
- Section for Science of Complex Systems, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria.,Complexity Science Hub Vienna, Vienna, Austria
| | - Thomas Niederkrotenthaler
- Department of Social and Preventive Medicine, Center for Public Health, Medical University of Vienna, Vienna, Austria
| | - Alexandra Kautzky-Willer
- Department of Internal Medicine III, Clinical Division of Endocrinology and Metabolism, Medical University of Vienna, Vienna, Austria.,Gender Institute, Gars am Kamp, Austria
| | - Peter Klimek
- Section for Science of Complex Systems, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria.,Complexity Science Hub Vienna, Vienna, Austria
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Gaudelet T, Malod-Dognin N, Sánchez-Valle J, Pancaldi V, Valencia A, Pržulj N. Unveiling new disease, pathway, and gene associations via multi-scale neural network. PLoS One 2020; 15:e0231059. [PMID: 32251458 PMCID: PMC7135208 DOI: 10.1371/journal.pone.0231059] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 03/14/2020] [Indexed: 12/16/2022] Open
Abstract
Diseases involve complex modifications to the cellular machinery. The gene expression profile of the affected cells contains characteristic patterns linked to a disease. Hence, new biological knowledge about a disease can be extracted from these profiles, improving our ability to diagnose and assess disease risks. This knowledge can be used for drug re-purposing, or by physicians to evaluate a patient’s condition and co-morbidity risk. Here, we consider differential gene expressions obtained by microarray technology for patients diagnosed with various diseases. Based on these data and cellular multi-scale organization, we aim at uncovering disease–disease, disease–gene and disease–pathway associations. We propose a neural network with structure based on the multi-scale organization of proteins in a cell into biological pathways. We show that this model is able to correctly predict the diagnosis for the majority of patients. Through the analysis of the trained model, we predict disease–disease, disease–pathway, and disease–gene associations and validate the predictions by comparisons to known interactions and literature search, proposing putative explanations for the predictions.
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Affiliation(s)
- Thomas Gaudelet
- Department of Computer Science, University College London, London, United Kingdom
| | | | | | - Vera Pancaldi
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Centre de Recherches en Cancérologie de Toulouse (CRCT), UMR1037 Inserm, ERL5294 CNRS, 31037, Toulouse, France
- University Paul Sabatier III, Toulouse, France
| | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- ICREA, Pg. Lluis Companys, Barcelona, Spain
| | - Nataša Pržulj
- Department of Computer Science, University College London, London, United Kingdom
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- ICREA, Pg. Lluis Companys, Barcelona, Spain
- * E-mail:
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6
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Haug N, Deischinger C, Gyimesi M, Kautzky-Willer A, Thurner S, Klimek P. High-risk multimorbidity patterns on the road to cardiovascular mortality. BMC Med 2020; 18:44. [PMID: 32151252 PMCID: PMC7063814 DOI: 10.1186/s12916-020-1508-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 02/03/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Multimorbidity, the co-occurrence of two or more diseases in one patient, is a frequent phenomenon. Understanding how different diseases condition each other over the lifetime of a patient could significantly contribute to personalised prevention efforts. However, most of our current knowledge on the long-term development of the health of patients (their disease trajectories) is either confined to narrow time spans or specific (sets of) diseases. Here, we aim to identify decisive events that potentially determine the future disease progression of patients. METHODS Health states of patients are described by algorithmically identified multimorbidity patterns (groups of included or excluded diseases) in a population-wide analysis of 9,000,000 patient histories of hospital diagnoses observed over 17 years. Over time, patients might acquire new diagnoses that change their health state; they describe a disease trajectory. We measure the age- and sex-specific risks for patients that they will acquire certain sets of diseases in the future depending on their current health state. RESULTS In the present analysis, the population is described by a set of 132 different multimorbidity patterns. For elderly patients, we find 3 groups of multimorbidity patterns associated with low (yearly in-hospital mortality of 0.2-0.3%), medium (0.3-1%) and high in-hospital mortality (2-11%). We identify combinations of diseases that significantly increase the risk to reach the high-mortality health states in later life. For instance, in men (women) aged 50-59 diagnosed with diabetes and hypertension, the risk for moving into the high-mortality region within 1 year is increased by the factor of 1.96 ± 0.11 (2.60 ± 0.18) compared with all patients of the same age and sex, respectively, and by the factor of 2.09 ± 0.12 (3.04 ± 0.18) if additionally diagnosed with metabolic disorders. CONCLUSIONS Our approach can be used both to forecast future disease burdens, as well as to identify the critical events in the careers of patients which strongly determine their disease progression, therefore constituting targets for efficient prevention measures. We show that the risk for cardiovascular diseases increases significantly more in females than in males when diagnosed with diabetes, hypertension and metabolic disorders.
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Affiliation(s)
- Nina Haug
- Section for Science of Complex Systems, CeMSIIS, Medical University of Vienna, Spitalgasse 23, Vienna, A-1090, Austria.,Complexity Science Hub Vienna, Josefstädter Straße 39, Vienna, A-1080, Austria
| | - Carola Deischinger
- Gender Medicine Unit, Division of Endocrinology and Metabolism, Department of Internal Medicine III, Medical University of Vienna, Spitalgasse 23, Vienna, A-1090, Austria
| | - Michael Gyimesi
- Gesundheit Österreich GmbH, Stubenring 6, Vienna, A-1010, Austria
| | - Alexandra Kautzky-Willer
- Gender Medicine Unit, Division of Endocrinology and Metabolism, Department of Internal Medicine III, Medical University of Vienna, Spitalgasse 23, Vienna, A-1090, Austria
| | - Stefan Thurner
- Section for Science of Complex Systems, CeMSIIS, Medical University of Vienna, Spitalgasse 23, Vienna, A-1090, Austria.,Complexity Science Hub Vienna, Josefstädter Straße 39, Vienna, A-1080, Austria.,IIASA, Schloßplatz 1, Laxenburg, A-2361, Austria.,Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, 85701, NM, USA
| | - Peter Klimek
- Section for Science of Complex Systems, CeMSIIS, Medical University of Vienna, Spitalgasse 23, Vienna, A-1090, Austria. .,Complexity Science Hub Vienna, Josefstädter Straße 39, Vienna, A-1080, Austria.
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7
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Kim MH, Banerjee S, Zhao Y, Wang F, Zhang Y, Zhu Y, DeFerio J, Evans L, Park SM, Pathak J. Association networks in a matched case-control design - Co-occurrence patterns of preexisting chronic medical conditions in patients with major depression versus their matched controls. J Biomed Inform 2018; 87:88-95. [PMID: 30300713 PMCID: PMC6262847 DOI: 10.1016/j.jbi.2018.09.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2018] [Revised: 09/25/2018] [Accepted: 09/28/2018] [Indexed: 01/09/2023]
Abstract
OBJECTIVE We present a method for comparing association networks in a matched case-control design, which provides a high-level comparison of co-occurrence patterns of features after adjusting for confounding factors. We demonstrate this approach by examining the differential distribution of chronic medical conditions in patients with major depressive disorder (MDD) compared to the distribution of these conditions in their matched controls. MATERIALS AND METHODS Newly diagnosed MDD patients were matched to controls based on their demographic characteristics, socioeconomic status, place of residence, and healthcare service utilization in the Korean National Health Insurance Service's National Sample Cohort. Differences in the networks of chronic medical conditions in newly diagnosed MDD cases treated with antidepressants, and their matched controls, were prioritized with a permutation test accounting for the false discovery rate. Sensitivity analyses for the associations between prioritized pairs of chronic medical conditions and new MDD diagnosis were performed with regression modeling. RESULTS By comparing the association networks of chronic medical conditions in newly diagnosed depression patients and their matched controls, five pairs of such conditions were prioritized among 105 possible pairs after controlling the false discovery rate at 5%. In sensitivity analyses using regression modeling, four out of the five prioritized pairs were statistically significant for the interaction terms. CONCLUSION Association networks in a matched case-control design can provide a high-level comparison of comorbid features after adjusting for confounding factors, thereby supplementing traditional clinical study approaches. We demonstrate the differential co-occurrence pattern of chronic medical conditions in patients with MDD and prioritize the chronic conditions that have statistically significant interactions in regression models for depression.
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Affiliation(s)
- Min-Hyung Kim
- Division of Health Informatics, Department of Health Policy and Research, Weill Cornell Medical College of Cornell University, NY, USA
| | - Samprit Banerjee
- Division of Biostatistics and Epidemiology, Department of Health Policy and Research, Weill Cornell Medical College of Cornell University, NY, USA
| | - Yize Zhao
- Division of Biostatistics and Epidemiology, Department of Health Policy and Research, Weill Cornell Medical College of Cornell University, NY, USA
| | - Fei Wang
- Division of Health Informatics, Department of Health Policy and Research, Weill Cornell Medical College of Cornell University, NY, USA
| | - Yiye Zhang
- Division of Health Informatics, Department of Health Policy and Research, Weill Cornell Medical College of Cornell University, NY, USA
| | - Yongjun Zhu
- Department of Library and Information Science, Sungkyungkwan University, Seoul, Republic of Korea
| | - Joseph DeFerio
- Division of Health Informatics, Department of Health Policy and Research, Weill Cornell Medical College of Cornell University, NY, USA
| | - Lauren Evans
- Division of Biostatistics and Epidemiology, Department of Health Policy and Research, Weill Cornell Medical College of Cornell University, NY, USA
| | - Sang Min Park
- Department of Family Medicine, Seoul National University Hospital, Seoul, Republic of Korea; Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Jyotishman Pathak
- Division of Health Informatics, Department of Health Policy and Research, Weill Cornell Medical College of Cornell University, NY, USA.
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8
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Wang T, Qiu RG, Yu M. Predictive Modeling of the Progression of Alzheimer's Disease with Recurrent Neural Networks. Sci Rep 2018; 8:9161. [PMID: 29907747 PMCID: PMC6003986 DOI: 10.1038/s41598-018-27337-w] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Accepted: 05/21/2018] [Indexed: 12/27/2022] Open
Abstract
The number of service visits of Alzheimer's disease (AD) patients is different from each other and their visit time intervals are non-uniform. Although the literature has revealed many approaches in disease progression modeling, they fail to leverage these time-relevant part of patients' medical records in predicting disease's future status. This paper investigates how to predict the AD progression for a patient's next medical visit through leveraging heterogeneous medical data. Data provided by the National Alzheimer's Coordinating Center includes 5432 patients with probable AD from August 31, 2005 to May 25, 2017. Long short-term memory recurrent neural networks (RNN) are adopted. The approach relies on an enhanced "many-to-one" RNN architecture to support the shift of time steps. Hence, the approach can deal with patients' various numbers of visits and uneven time intervals. The results show that the proposed approach can be utilized to predict patients' AD progressions on their next visits with over 99% accuracy, significantly outperforming classic baseline methods. This study confirms that RNN can effectively solve the AD progression prediction problem by fully leveraging the inherent temporal and medical patterns derived from patients' historical visits. More promisingly, the approach can be customarily applied to other chronic disease progression problems.
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Affiliation(s)
- Tingyan Wang
- Health Care Services Research Center, Department of Industrial Engineering, Tsinghua University, Beijing, 100084, China
- Big Data Lab, Division of Engineering and Information Science, The Pennsylvania State University, Malvern, PA, 19355, USA
| | - Robin G Qiu
- Big Data Lab, Division of Engineering and Information Science, The Pennsylvania State University, Malvern, PA, 19355, USA.
| | - Ming Yu
- Health Care Services Research Center, Department of Industrial Engineering, Tsinghua University, Beijing, 100084, China
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9
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Tényi Á, Vela E, Cano I, Cleries M, Monterde D, Gomez-Cabrero D, Roca J. Risk and temporal order of disease diagnosis of comorbidities in patients with COPD: a population health perspective. BMJ Open Respir Res 2018; 5:e000302. [PMID: 29955364 PMCID: PMC6018856 DOI: 10.1136/bmjresp-2018-000302] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Revised: 05/22/2018] [Indexed: 02/06/2023] Open
Abstract
Introduction Comorbidities in patients with chronic obstructive pulmonary disease (COPD) generate a major burden on healthcare. Identification of cost-effective strategies aiming at preventing and enhancing management of comorbid conditions in patients with COPD requires deeper knowledge on epidemiological patterns and on shared biological pathways explaining co-occurrence of diseases. Methods The study assesses the co-occurrence of several chronic conditions in patients with COPD using two different datasets: Catalan Healthcare Surveillance System (CHSS) (ES, 1.4 million registries) and Medicare (USA, 13 million registries). Temporal order of disease diagnosis was analysed in the CHSS dataset. Results The results demonstrate higher prevalence of most of the diseases, as comorbid conditions, in elderly (>65) patients with COPD compared with non-COPD subjects, an effect observed in both CHSS and Medicare datasets. Analysis of temporal order of disease diagnosis showed that comorbid conditions in elderly patients with COPD tend to appear after the diagnosis of the obstructive disease, rather than before it. Conclusion The results provide a population health perspective of the comorbidity challenge in patients with COPD, indicating the increased risk of developing comorbid conditions in these patients. The research reinforces the need for novel approaches in the prevention and management of comorbidities in patients with COPD to effectively reduce the overall burden of the disease on these patients.
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Affiliation(s)
- Ákos Tényi
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain.,Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Madrid, Spain
| | - Emili Vela
- Unitat d'Informació i Coneixement, Servei Catala de la Salut de la Generalitat de Catalunya, Barcelona, Catalunya, Spain
| | - Isaac Cano
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain.,Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Madrid, Spain
| | - Montserrat Cleries
- Unitat d'Informació i Coneixement, Servei Catala de la Salut de la Generalitat de Catalunya, Barcelona, Catalunya, Spain
| | - David Monterde
- Serveis Centrals, Institut Català de la Salut, Barcelona, Spain
| | - David Gomez-Cabrero
- Mucosal and Salivary Biology Division, King's College London Dental Institute, London, UK.,Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine Solna, Karolinska Institutet, Karolinska University Hospital and Science for Life Laboratory, Stockholm, Sweden.,Translational Bioinformatics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, Spain
| | - Josep Roca
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain.,Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Madrid, Spain
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10
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Brunson JC, Laubenbacher RC. Applications of network analysis to routinely collected health care data: a systematic review. J Am Med Inform Assoc 2018; 25:210-221. [PMID: 29025116 PMCID: PMC6664849 DOI: 10.1093/jamia/ocx052] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Revised: 04/18/2017] [Accepted: 04/23/2017] [Indexed: 01/21/2023] Open
Abstract
Objective To survey network analyses of datasets collected in the course of routine operations in health care settings and identify driving questions, methods, needs, and potential for future research. Materials and Methods A search strategy was designed to find studies that applied network analysis to routinely collected health care datasets and was adapted to 3 bibliographic databases. The results were grouped according to a thematic analysis of their settings, objectives, data, and methods. Each group received a methodological synthesis. Results The search found 189 distinct studies reported before August 2016. We manually partitioned the sample into 4 groups, which investigated institutional exchange, physician collaboration, clinical co-occurrence, and workplace interaction networks. Several robust and ongoing research programs were discerned within (and sometimes across) the groups. Little interaction was observed between these programs, despite conceptual and methodological similarities. Discussion We use the literature sample to inform a discussion of good practice at this methodological interface, including the concordance of motivations, study design, data, and tools and the validation and standardization of techniques. We then highlight instances of positive feedback between methodological development and knowledge domains and assess the overall cohesion of the sample.
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Gomez-Cabrero D, Menche J, Vargas C, Cano I, Maier D, Barabási AL, Tegnér J, Roca J. From comorbidities of chronic obstructive pulmonary disease to identification of shared molecular mechanisms by data integration. BMC Bioinformatics 2016; 17:441. [PMID: 28185567 PMCID: PMC5133493 DOI: 10.1186/s12859-016-1291-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Background Deep mining of healthcare data has provided maps of comorbidity relationships between diseases. In parallel, integrative multi-omics investigations have generated high-resolution molecular maps of putative relevance for understanding disease initiation and progression. Yet, it is unclear how to advance an observation of comorbidity relations (one disease to others) to a molecular understanding of the driver processes and associated biomarkers. Results Since Chronic Obstructive Pulmonary disease (COPD) has emerged as a central hub in temporal comorbidity networks, we developed a systematic integrative data-driven framework to identify shared disease-associated genes and pathways, as a proxy for the underlying generative mechanisms inducing comorbidity. We integrated records from approximately 13 M patients from the Medicare database with disease-gene maps that we derived from several resources including a semantic-derived knowledge-base. Using rank-based statistics we not only recovered known comorbidities but also discovered a novel association between COPD and digestive diseases. Furthermore, our analysis provides the first set of COPD co-morbidity candidate biomarkers, including IL15, TNF and JUP, and characterizes their association to aging and life-style conditions, such as smoking and physical activity. Conclusions The developed framework provides novel insights in COPD and especially COPD co-morbidity associated mechanisms. The methodology could be used to discover and decipher the molecular underpinning of other comorbidity relationships and furthermore, allow the identification of candidate co-morbidity biomarkers. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1291-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- David Gomez-Cabrero
- Department of Medicine, Karolinska Institutet, Unit of Computational Medicine, Stockholm, 171 77, Sweden. .,Karolinska Institutet, Center for Molecular Medicine, Stockholm, 171 77, Sweden. .,Department of Medicine, Unit of Clinical Epidemiology, Karolinska University Hospital, Solna, L8, 17176, Sweden. .,Science for Life Laboratory, Solna, 17121, Sweden. .,Mucosal and Salivary Biology Division, King's College London Dental Institute, London, UK.
| | - Jörg Menche
- Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, MA, USA.,Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.,Center for Network Science, Central European University, Budapest, Hungary
| | - Claudia Vargas
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clinic de Barcelona, Universitat de Barcelona, Barcelona, Spain.,Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Madrid, Spain
| | - Isaac Cano
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clinic de Barcelona, Universitat de Barcelona, Barcelona, Spain.,Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Madrid, Spain
| | | | - Albert-László Barabási
- Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, MA, USA.,Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.,Center for Network Science, Central European University, Budapest, Hungary.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jesper Tegnér
- Department of Medicine, Karolinska Institutet, Unit of Computational Medicine, Stockholm, 171 77, Sweden.,Karolinska Institutet, Center for Molecular Medicine, Stockholm, 171 77, Sweden.,Department of Medicine, Unit of Clinical Epidemiology, Karolinska University Hospital, Solna, L8, 17176, Sweden.,Science for Life Laboratory, Solna, 17121, Sweden
| | - Josep Roca
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clinic de Barcelona, Universitat de Barcelona, Barcelona, Spain. .,Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Madrid, Spain.
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