Modeling Asthma Exacerbations from Electronic Health Records.
Journal
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
ISSN: 2153-4063
Titre abrégé: AMIA Jt Summits Transl Sci Proc
Pays: United States
ID NLM: 101539486
Informations de publication
Date de publication:
2020
2020
Historique:
entrez:
2
6
2020
pubmed:
2
6
2020
medline:
2
6
2020
Statut:
epublish
Résumé
Asthma is a prevalent chronic respiratory condition, and acute exacerbations represent a significant fraction of the economic and health-related costs associated with asthma. We present results from a novel study that is focused on modeling asthma exacerbations from data contained in patients' electronic health records. This work makes the following contributions: (i) we develop an algorithm for phenotyping asthma exacerbations from EHRs, (ii) we determine that models learned via supervised learning approaches can predict asthma exacerbations in the near future (AUC ≈ 0.77), and (iii) we develop an approach, based on mixtures of semi-Markov models, that is able to identify subpopula-tions of asthma patients sharing distinct temporal and seasonal patterns in their exacerbation susceptibility.
Types de publication
Journal Article
Langues
eng
Pagination
98-107Informations de copyright
©2020 AMIA - All rights reserved.
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