Multitask Learning With Recurrent Neural Networks for Acute Respiratory Distress Syndrome Prediction Using Only Electronic Health Record Data: Model Development and Validation Study

• Published in: JMIR medical informatics Vol. 10; no. 6; p. e36202
• Main Authors: Lam, Carson, Thapa, Rahul, Maharjan, Jenish, Rahmani, Keyvan, Tso, Chak Foon, Singh, Navan Preet, Casie Chetty, Satish, Mao, Qingqing
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 15.06.2022
• Subjects: Datasets; Electronic health records; Hypoxemia; Mortality; Neural networks; Original Paper; Validation studies; diagnostic criteria; prediction model; recurrent neural network; neural networks; multitask learning; COVID-19; deep learning; risk outcome; clinical decision support; electronic health record; ARDS; health care; respiratory distress

Acute respiratory distress syndrome (ARDS) is a condition that is often considered to have broad and subjective diagnostic criteria and is associated with significant mortality and morbidity. Early and accurate prediction of ARDS and related conditions such as hypoxemia and sepsis could allow timely...