Using machine learning to model older adult inpatient trajectories from electronic health records data

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 ea...

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Detalles Bibliográficos
Autores principales: Herrero-Zazo, Maria, Fitzgerald, Tomas, Taylor, Vince, Street, Helen, Chaudhry, Afzal N., Bradley, John R., Birney, Ewan, Keevil, Victoria L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9860485/
https://www.ncbi.nlm.nih.gov/pubmed/36691609
http://dx.doi.org/10.1016/j.isci.2022.105876

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9860485/
https://www.ncbi.nlm.nih.gov/pubmed/36691609
http://dx.doi.org/10.1016/j.isci.2022.105876

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