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Oh W, Jayaraman P, Tandon P, Chaddha US, Kovatch P, Charney AW, Glicksberg BS, Nadkarni GN. A novel method leveraging time series data to improve subphenotyping and application in critically ill patients with COVID-19. Artif Intell Med 2024; 148:102750. [PMID: 38325922 PMCID: PMC10864255 DOI: 10.1016/j.artmed.2023.102750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 12/12/2023] [Accepted: 12/14/2023] [Indexed: 02/09/2024]
Abstract
Computational subphenotyping, a data-driven approach to understanding disease subtypes, is a prominent topic in medical research. Numerous ongoing studies are dedicated to developing advanced computational subphenotyping methods for cross-sectional data. However, the potential of time-series data has been underexplored until now. Here, we propose a Multivariate Levenshtein Distance (MLD) that can account for address correlation in multiple discrete features over time-series data. Our algorithm has two distinct components: it integrates an optimal threshold score to enhance the sensitivity in discriminating between pairs of instances, and the MLD itself. We have applied the proposed distance metrics on the k-means clustering algorithm to derive temporal subphenotypes from time-series data of biomarkers and treatment administrations from 1039 critically ill patients with COVID-19 and compare its effectiveness to standard methods. In conclusion, the Multivariate Levenshtein Distance metric is a novel method to quantify the distance from multiple discrete features over time-series data and demonstrates superior clustering performance among competing time-series distance metrics.
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Affiliation(s)
- Wonsuk Oh
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Pushkala Jayaraman
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Pranai Tandon
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Udit S Chaddha
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Patricia Kovatch
- Department of Scientific Computing, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alexander W Charney
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Character Biosciences, New York, NY, USA
| | - Girish N Nadkarni
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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2
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Tsuyuzaki K, Yoshida N, Ishikawa T, Goshima Y, Kawakami E. Non-negative tensor factorization workflow for time series biomedical data. STAR Protoc 2023; 4:102318. [PMID: 37421614 PMCID: PMC10511860 DOI: 10.1016/j.xpro.2023.102318] [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: 02/10/2023] [Revised: 03/28/2023] [Accepted: 04/26/2023] [Indexed: 07/10/2023] Open
Abstract
Non-negative tensor factorization (NTF) enables the extraction of a small number of latent components from high-dimensional biomedical data. However, NTF requires many steps, which is a hurdle to implementation. Here, we provide a protocol for TensorLyCV, an easy to run and reproducible NTF analysis pipeline using Snakemake workflow management system and Docker container. Using vaccine adverse reaction data as an example, we describe steps for data processing, tensor decomposition, optimal rank parameter estimation, and visualization of factor matrices. For complete details on the use and execution of this protocol, please refer to Kei Ikeda et al.1.
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Affiliation(s)
- Koki Tsuyuzaki
- Laboratory for Bioinformatics Research, RIKEN Center for Biosystems Dynamics Research, Wako, Saitama 351-0198, Japan; Japan Science and Technology Agency, PRESTO, 7 Gobancho, Chiyoda-ku, Tokyo 102-0076, Japan.
| | - Naoki Yoshida
- Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Tetsuo Ishikawa
- Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan; Advanced Data Science Project (ADSP), RIKEN Information R&D and Strategy Headquarters, Yokohama, Kanagawa 230-0045, Japan; Department of Extended Intelligence for Medicine, The Ishii-Ishibashi Laboratory, Keio University School of Medicine, Shinjuku-ku, Tokyo 160-8582, Japan
| | - Yuki Goshima
- Advanced Data Science Project (ADSP), RIKEN Information R&D and Strategy Headquarters, Yokohama, Kanagawa 230-0045, Japan
| | - Eiryo Kawakami
- Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan; Advanced Data Science Project (ADSP), RIKEN Information R&D and Strategy Headquarters, Yokohama, Kanagawa 230-0045, Japan; NEXT-Ganken Program, Japanese Foundation for Cancer Research (JFCR), Koto Ward, Tokyo 135-8550, Japan; Institute for Advanced Academic Research (IAAR), Chiba University, Chiba 260-8670, Japan.
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3
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Adali T, Kantar F, Akhonda MABS, Strother S, Calhoun VD, Acar E. Reproducibility in Matrix and Tensor Decompositions: Focus on Model Match, Interpretability, and Uniqueness. IEEE SIGNAL PROCESSING MAGAZINE 2022; 39:8-24. [PMID: 36337436 PMCID: PMC9635492 DOI: 10.1109/msp.2022.3163870] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Affiliation(s)
- Tülay Adali
- Department of CSEE, University of Maryland, Baltimore County, Baltimore, MD, USA
| | - Furkan Kantar
- Department of CSEE, University of Maryland, Baltimore County, Baltimore, MD, USA
| | | | - Stephen Strother
- Rotman Research Center, Baycrest, and Department of Medical Biophysics, University of Toronto, ON, Canada
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA
| | - Evrim Acar
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
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4
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Spadon G, Hong S, Brandoli B, Matwin S, Rodrigues-Jr JF, Sun J. Pay Attention to Evolution: Time Series Forecasting with Deep Graph-Evolution Learning. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2021; PP:5368-5384. [PMID: 33905327 DOI: 10.1109/tpami.2021.3076155] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Time-series forecasting is one of the most active research topics in artificial intelligence. A still open gap in that literature is that statistical and ensemble learning approaches systematically present lower predictive performance than deep learning methods. They generally disregard the data sequence aspect entangled with multivariate data represented in more than one time series. Conversely, this work presents a novel neural network architecture for time-series forecasting that combines the power of graph evolution with deep recurrent learning on distinct data distributions; we named our method Recurrent Graph Evolution Neural Network (ReGENN). The idea is to infer multiple multivariate relationships between co-occurring time-series by assuming that the temporal data depends not only on inner variables and intra-temporal relationships (i.e., observations from itself) but also on outer variables and inter-temporal relationships (i.e., observations from other-selves). An extensive set of experiments was conducted comparing ReGENN with dozens of ensemble methods and classical statistical ones, showing sound improvement of up to 64.87% over the competing algorithms. Furthermore, we present an analysis of the intermediate weights arising from ReGENN, showing that by looking at inter and intra-temporal relationships simultaneously, time-series forecasting is majorly improved if paying attention to how multiple multivariate data synchronously evolve.
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5
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Hassaine A, Canoy D, Solares JRA, Zhu Y, Rao S, Li Y, Zottoli M, Rahimi K, Salimi-Khorshidi G. Learning multimorbidity patterns from electronic health records using Non-negative Matrix Factorisation. J Biomed Inform 2020; 112:103606. [PMID: 33127447 DOI: 10.1016/j.jbi.2020.103606] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 08/01/2020] [Accepted: 10/16/2020] [Indexed: 11/29/2022]
Abstract
Multimorbidity, or the presence of several medical conditions in the same individual, has been increasing in the population - both in absolute and relative terms. Nevertheless, multimorbidity remains poorly understood, and the evidence from existing research to describe its burden, determinants and consequences has been limited. Previous studies attempting to understand multimorbidity patterns are often cross-sectional and do not explicitly account for multimorbidity patterns' evolution over time; some of them are based on small datasets and/or use arbitrary and narrow age ranges; and those that employed advanced models, usually lack appropriate benchmarking and validations. In this study, we (1) introduce a novel approach for using Non-negative Matrix Factorisation (NMF) for temporal phenotyping (i.e., simultaneously mining disease clusters and their trajectories); (2) provide quantitative metrics for the evaluation of these clusters and trajectories; and (3) demonstrate how the temporal characteristics of the disease clusters that result from our model can help mine multimorbidity networks and generate new hypotheses for the emergence of various multimorbidity patterns over time. We trained and evaluated our models on one of the world's largest electronic health records (EHR) datasets, containing more than 7 million patients, from which over 2 million where relevant to, and hence included in this study.
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Affiliation(s)
- Abdelaali Hassaine
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Dexter Canoy
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Jose Roberto Ayala Solares
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Yajie Zhu
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom
| | - Shishir Rao
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom
| | - Yikuan Li
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom
| | - Mariagrazia Zottoli
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Kazem Rahimi
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
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6
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Hassaine A, Salimi-Khorshidi G, Canoy D, Rahimi K. Untangling the complexity of multimorbidity with machine learning. Mech Ageing Dev 2020; 190:111325. [PMID: 32768443 PMCID: PMC7493712 DOI: 10.1016/j.mad.2020.111325] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 07/28/2020] [Accepted: 07/30/2020] [Indexed: 12/20/2022]
Abstract
The prevalence of multimorbidity has been increasing in recent years, posing a major burden for health care delivery and service. Understanding its determinants and impact is proving to be a challenge yet it offers new opportunities for research to go beyond the study of diseases in isolation. In this paper, we review how the field of machine learning provides many tools for addressing research challenges in multimorbidity. We highlight recent advances in promising methods such as matrix factorisation, deep learning, and topological data analysis and how these can take multimorbidity research beyond cross-sectional, expert-driven or confirmatory approaches to gain a better understanding of evolving patterns of multimorbidity. We discuss the challenges and opportunities of machine learning to identify likely causal links between previously poorly understood disease associations while giving an estimate of the uncertainty on such associations. We finally summarise some of the challenges for wider clinical adoption of machine learning research tools and propose some solutions.
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Affiliation(s)
- Abdelaali Hassaine
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom; Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom
| | - Gholamreza Salimi-Khorshidi
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom; Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom
| | - Dexter Canoy
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom; Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom
| | - Kazem Rahimi
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom; Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom.
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7
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Yin K, Afshar A, Ho JC, Cheung WK, Zhang C, Sun J. LogPar: Logistic PARAFAC2 Factorization for Temporal Binary Data with Missing Values. KDD : PROCEEDINGS. INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING 2020; 2020:1625-1635. [PMID: 34109054 DOI: 10.1145/3394486.3403213] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Binary data with one-class missing values are ubiquitous in real-world applications. They can be represented by irregular tensors with varying sizes in one dimension, where value one means presence of a feature while zero means unknown (i.e., either presence or absence of a feature). Learning accurate low-rank approximations from such binary irregular tensors is a challenging task. However, none of the existing models developed for factorizing irregular tensors take the missing values into account, and they assume Gaussian distributions, resulting in a distribution mismatch when applied to binary data. In this paper, we propose Logistic PARAFAC2 (LogPar) by modeling the binary irregular tensor with Bernoulli distribution parameterized by an underlying real-valued tensor. Then we approximate the underlying tensor with a positive-unlabeled learning loss function to account for the missing values. We also incorporate uniqueness and temporal smoothness regularization to enhance the interpretability. Extensive experiments using large-scale real-world datasets show that LogPar outperforms all baselines in both irregular tensor completion and downstream predictive tasks. For the irregular tensor completion, LogPar achieves up to 26% relative improvement compared to the best baseline. Besides, LogPar obtains relative improvement of 13.2% for heart failure prediction and 14% for mortality prediction on average compared to the state-of-the-art PARAFAC2 models.
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Affiliation(s)
| | | | | | | | | | - Jimeng Sun
- University of Illinois, Urbana-Champaign
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8
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Zhuo L, Li K, Li H, Peng J, Li K. An online and generalized non-negativity constrained model for large-scale sparse tensor estimation on multi-GPU. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.02.068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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9
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Perros I, Yan X, Jones JB, Sun J, Stewart WF. Using the PARAFAC2 tensor factorization on EHR audit data to understand PCP desktop work. J Biomed Inform 2019; 101:103312. [PMID: 31627022 DOI: 10.1016/j.jbi.2019.103312] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 10/08/2019] [Accepted: 10/13/2019] [Indexed: 11/26/2022]
Abstract
BACKGROUND Activity or audit log data are required for EHR privacy and security management but may also be useful for understanding desktop workflow. OBJECTIVE We determined if the EHR audit log file, a rich source of complex time-stamped data on desktop activities, could be processed to derive primary care provider (PCP) level workflow measures. METHODS We analyzed audit log data on 876 PCPs across 17,455 ambulatory care encounters that generated 578,394 time-stamped records. Each individual record represents a user interaction (e.g., point and click) that reflects all or part of a specific activity (e.g., order entry access). No dictionary exists to define how to combine clusters of sequential audit log records to represent identifiable PCP tasks. We determined if PARAFAC2 tensor factorization could: (1) learn to identify audit log record clusters that specifically represent defined PCP tasks; and (2) identify variation in how tasks are completed without the need for ground-truth labels. To interpret the result, we used the following PARAFAC2 factors: a matrix representing the task definitions and a matrix containing the frequency measure of each task for each encounter. RESULTS PARAFAC2 automatically identified 4 clusters of audit log records that represent 4 common clinical encounter tasks: (1) medications' access, (2) notes' access, (3) order entry access, and (4) diagnosis modification. PARAFAC2 also identified the most common variants in how PCPs accomplish these tasks. It discovered variation in how the notes' access task was done, including identification of 9 distinct variants of notes access that explained 77% of the input data variation for notes. The discovered variants mapped to two known workflows for notes' access and to two distinct PCP user groups who accessed notes by either using the Visit Navigator or the Wrap-Up option. CONCLUSIONS Our results demonstrate that EHR audit log data can be rapidly processed to create higher-level constructed features that represent time-stamped PCP tasks.
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Affiliation(s)
- Ioakeim Perros
- Georgia Institute of Technology, Atlanta, GA, United States
| | - Xiaowei Yan
- Research Development & Dissemination, Sutter Health, Walnut Creek, CA, United States
| | - J B Jones
- Research Development & Dissemination, Sutter Health, Walnut Creek, CA, United States
| | - Jimeng Sun
- Georgia Institute of Technology, Atlanta, GA, United States
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10
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Zhao J, Zhang Y, Schlueter DJ, Wu P, Eric Kerchberger V, Trent Rosenbloom S, Wells QS, Feng Q, Denny JC, Wei WQ. Detecting time-evolving phenotypic topics via tensor factorization on electronic health records: Cardiovascular disease case study. J Biomed Inform 2019; 98:103270. [PMID: 31445983 PMCID: PMC6783385 DOI: 10.1016/j.jbi.2019.103270] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 07/10/2019] [Accepted: 08/16/2019] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Discovering subphenotypes of complex diseases can help characterize disease cohorts for investigative studies aimed at developing better diagnoses and treatments. Recent advances in unsupervised machine learning on electronic health record (EHR) data have enabled researchers to discover phenotypes without input from domain experts. However, most existing studies have ignored time and modeled diseases as discrete events. Uncovering the evolution of phenotypes - how they emerge, evolve and contribute to health outcomes - is essential to define more precise phenotypes and refine the understanding of disease progression. Our objective was to assess the benefits of an unsupervised approach that incorporates time to model diseases as dynamic processes in phenotype discovery. METHODS In this study, we applied a constrained non-negative tensor-factorization approach to characterize the complexity of cardiovascular disease (CVD) patient cohort based on longitudinal EHR data. Through tensor-factorization, we identified a set of phenotypic topics (i.e., subphenotypes) that these patients established over the 10 years prior to the diagnosis of CVD, and showed the progress pattern. For each identified subphenotype, we examined its association with the risk for adverse cardiovascular outcomes estimated by the American College of Cardiology/American Heart Association Pooled Cohort Risk Equations, a conventional CVD-risk assessment tool frequently used in clinical practice. Furthermore, we compared the subsequent myocardial infarction (MI) rates among the six most prevalent subphenotypes using survival analysis. RESULTS From a cohort of 12,380 adult CVD individuals with 1068 unique PheCodes, we successfully identified 14 subphenotypes. Through the association analysis with estimated CVD risk for each subtype, we found some phenotypic topics such as Vitamin D deficiency and depression, Urinary infections cannot be explained by the conventional risk factors. Through a survival analysis, we found markedly different risks of subsequent MI following the diagnosis of CVD among the six most prevalent topics (p < 0.0001), indicating these topics may capture clinically meaningful subphenotypes of CVD. CONCLUSION This study demonstrates the potential benefits of using tensor-decomposition to model diseases as dynamic processes from longitudinal EHR data. Our results suggest that this data-driven approach may potentially help researchers identify complex and chronic disease subphenotypes in precision medicine research.
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Affiliation(s)
- Juan Zhao
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yun Zhang
- Fixed Income Division, Morgan Stanley & Co LLC, New York, NY, USA
| | - David J Schlueter
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Patrick Wu
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; Medical Scientist Training Program, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Vern Eric Kerchberger
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; Division of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - S Trent Rosenbloom
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Quinn S Wells
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - QiPing Feng
- Division of Clinical Pharmacology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
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