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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...
Autores principales: | Lam, Carson, Tso, Chak Foon, Green-Saxena, Abigail, Pellegrini, Emily, Iqbal, Zohora, Evans, Daniel, Hoffman, Jana, Calvert, Jacob, Mao, Qingqing, Das, Ritankar |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2021
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Materias: | |
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 |
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