Deep representation learning from electronic medical records identifies distinct symptom based subtypes and progression patterns for COVID-19 prognosis.

Qiguang ZhengQifan ShenZixin ShuKai ChangKunyu ZhongYuhang YanJia KeJingjing HuangRui SuJianan XiaXuezhong Zhou

Published in: International journal of medical informatics (2024)

This study has proposed a clinical meaningful approach by utilizing the deep representation learning and real-world EMR data containing symptom phenotypes to identify the COVID-19 subtypes and their progression patterns. The results would be potentially useful to help improve the precise stratification and management of acute infectious diseases.

Keyphrases

infectious diseases
coronavirus disease
sars cov
liver failure
genome wide
respiratory syndrome coronavirus
electronic health record
respiratory failure
drug induced
big data
intensive care unit
neural network
aortic dissection