Semisupervised Deep Learning Techniques for Predicting Acute Respiratory Distress Syndrome From Time-Series Clinical Data: Model Development and Validation Study

Semisupervised Deep Learning Techniques for Predicting Acute Respiratory Distress Syndrome From Time-Series Clinical Data: Model Development and Validation Study

BackgroundA high number of patients who are hospitalized with COVID-19 develop acute respiratory distress syndrome (ARDS). ObjectiveIn response to the need for clinical decision support tools to help manage the next pandemic during the early stages (ie, when limit...

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Bibliographic Details
Main Authors: Carson Lam, Chak Foon Tso, Abigail Green-Saxena, Emily Pellegrini, Zohora Iqbal, Daniel Evans, Jana Hoffman, Jacob Calvert, Qingqing Mao, Ritankar Das
Format: Article
Language:English
Published: JMIR Publications 2021-09-01
Series:JMIR Formative Research
Online Access:https://formative.jmir.org/2021/9/e28028

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https://formative.jmir.org/2021/9/e28028

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