Learning multimorbidity patterns from electronic health records using Non-negative Matrix Factorisation
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...
| Auteurs principaux: | , , , , , , , , |
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| Format: | Journal article |
| Langue: | English |
| Publié: |
Elsevier
2020
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| _version_ | 1826289525660319744 |
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| author | Hassaine, A Canoy, D Solares, JRA Zhu, Y Rao, S Li, Y Zottoli, M Rahimi, K Salimi-Khorshidi, G |
| author_facet | Hassaine, A Canoy, D Solares, JRA Zhu, Y Rao, S Li, Y Zottoli, M Rahimi, K Salimi-Khorshidi, G |
| author_sort | Hassaine, A |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T02:30:12Z |
| format | Journal article |
| id | oxford-uuid:a6fa5061-e053-4bec-ae1c-1720c0170089 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T02:30:12Z |
| publishDate | 2020 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:a6fa5061-e053-4bec-ae1c-1720c01700892022-03-27T02:51:18ZLearning multimorbidity patterns from electronic health records using Non-negative Matrix FactorisationJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:a6fa5061-e053-4bec-ae1c-1720c0170089EnglishSymplectic ElementsElsevier2020Hassaine, ACanoy, DSolares, JRAZhu, YRao, SLi, YZottoli, MRahimi, KSalimi-Khorshidi, GMultimorbidity, 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. |
| spellingShingle | 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 |
| title | Learning multimorbidity patterns from electronic health records using Non-negative Matrix Factorisation |
| title_full | Learning multimorbidity patterns from electronic health records using Non-negative Matrix Factorisation |
| title_fullStr | Learning multimorbidity patterns from electronic health records using Non-negative Matrix Factorisation |
| title_full_unstemmed | Learning multimorbidity patterns from electronic health records using Non-negative Matrix Factorisation |
| title_short | Learning multimorbidity patterns from electronic health records using Non-negative Matrix Factorisation |
| title_sort | learning multimorbidity patterns from electronic health records using non negative matrix factorisation |
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