Using machine learning to model older adult inpatient trajectories from electronic health records data.
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ABSTRACT:
SUBMITTER: Herrero-Zazo M
PROVIDER: S-EPMC9860485 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Using machine learning to model older adult inpatient trajectories from electronic health records data.
Herrero-Zazo Maria M Fitzgerald Tomas T Taylor Vince V Street Helen H Chaudhry Afzal N AN Bradley John R JR Birney Ewan E Keevil Victoria L VL
iScience 20221224 1
Electronic Health Records (EHR) data can provide novel insights into inpatient trajectories. Blood tests and vital signs from de-identified patients' hospital admission episodes (AE) were represented as multivariate time-series (MVTS) to train unsupervised Hidden Markov Models (HMM) and represent each AE day as one of 17 states. All HMM states were clinically interpreted based on their patterns of MVTS variables and relationships with clinical information. Visualization differentiated patients p ...[more]
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