A novel method leveraging time series data to improve subphenotyping and application in critically ill patients with COVID-19-Reference-Cited by-同舟云学术

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A novel method leveraging time series data to improve subphenotyping and application in critically ill patients with COVID-19

Published:2024-02 Issue: Volume:148 Page:102750
ISSN:0933-3657
Container-title:Artificial Intelligence in Medicine
language:en
Short-container-title:Artificial Intelligence in Medicine

Author:

Oh Wonsuk,Jayaraman Pushkala,Tandon Pranai,Chaddha Udit S.,Kovatch Patricia,Charney Alexander W.,Glicksberg Benjamin S.,Nadkarni Girish N.

Funder

National Institutes of Health

Icahn School of Medicine at Mount Sinai

Publisher

Elsevier BV

Subject

Artificial Intelligence,Medicine (miscellaneous)

Reference54 articles.

1. Machine learning approaches for electronic health records phenotyping: a methodical review;Yang;J Am Med Informatics Assoc,2023

2. Trends and opportunities in computable clinical phenotyping: a scoping review;He;J Biomed Inform,2023

3. Deep representation learning of patient data from Electronic Health Records (EHR): a systematic review;Si;J Biomed Inform,2021

4. High-throughput phenotyping with temporal sequences;Estiri;J Am Med Informatics Assoc,2021

5. Phe2vec: automated disease phenotyping based on unsupervised embeddings from electronic health records;De Freitas;Patterns,2021

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