TY  - JOUR
AU  - Lam, Carson
AU  - Tso, Chak Foon
AU  - Green-Saxena, Abigail
AU  - Pellegrini, Emily
AU  - Iqbal, Zohora
AU  - Evans, Daniel
AU  - Hoffman, Jana
AU  - Calvert, Jacob
AU  - Mao, Qingqing
AU  - Das, Ritankar
PY  - 2021
DA  - 2021/9/14
TI  - Semisupervised Deep Learning Techniques for Predicting Acute Respiratory Distress Syndrome From Time-Series Clinical Data: Model Development and Validation Study
JO  - JMIR Form Res
SP  - e28028
VL  - 5
IS  - 9
KW  - acute respiratory distress syndrome
KW  - COVID-19
KW  - semisupervised learning
KW  - deep learning
KW  - machine learning
KW  - algorithm
KW  - prediction
KW  - decision support
AB  - 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 developed machine learning algorithms that use semisupervised learning (SSL) techniques to predict ARDS development in general and COVID-19 populations based on limited labeled data. Methods: SSL techniques were applied to 29,127 encounters with patients who were admitted to 7 US hospitals from May 1, 2019, to May 1, 2021. A recurrent neural network that used a time series of electronic health record data was applied to data that were collected when a patient’s peripheral oxygen saturation level fell below the normal range (<97%) to predict the subsequent development of ARDS during the remaining duration of patients’ hospital stay. Model performance was assessed with the area under the receiver operating characteristic curve and area under the precision recall curve of an external hold-out test set. Results: For the whole data set, the median time between the first peripheral oxygen saturation measurement of <97% and subsequent respiratory failure was 21 hours. The area under the receiver operating characteristic curve for predicting subsequent ARDS development was 0.73 when the model was trained on a labeled data set of 6930 patients, 0.78 when the model was trained on the labeled data set that had been augmented with the unlabeled data set of 16,173 patients by using SSL techniques, and 0.84 when the model was trained on the entire training set of 23,103 labeled patients. Conclusions: In the context of using time-series inpatient data and a careful model training design, unlabeled data can be used to improve the performance of machine learning models when labeled data for predicting ARDS development are scarce or expensive. 
SN  - 2561-326X
UR  - https://formative.jmir.org/2021/9/e28028
UR  - https://doi.org/10.2196/28028
UR  - http://www.ncbi.nlm.nih.gov/pubmed/34398784
DO  - 10.2196/28028
ID  - info:doi/10.2196/28028
ER  -