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

BACKGROUND: A high number of patients who are hospitalized with COVID-19 develop acute respiratory distress syndrome (ARDS). OBJECTIVE: In response to the need for clinical decision support tools to help manage the next pandemic during the early stages (ie, when limited labeled data are present), we...

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Detalles Bibliográficos
Autores principales: Lam, Carson, Tso, Chak Foon, Green-Saxena, Abigail, Pellegrini, Emily, Iqbal, Zohora, Evans, Daniel, Hoffman, Jana, Calvert, Jacob, Mao, Qingqing, Das, Ritankar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2021
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8447921/
https://www.ncbi.nlm.nih.gov/pubmed/34398784
http://dx.doi.org/10.2196/28028

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8447921/
https://www.ncbi.nlm.nih.gov/pubmed/34398784
http://dx.doi.org/10.2196/28028

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