Relational Learning Improves Prediction of Mortality in COVID-19 in the Intensive Care Unit.

Autor:	Wanyan T; Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA, and the School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47405 USA , and also with the School of Information, University of Texas at Austin, Austin, TX 78712 USA., Vaid A; Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA., De Freitas JK; Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA, and also with the Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA., Somani S; Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA., Miotto R; Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA, and also with the Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA., Nadkarni GN; Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA, and also with the Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA., Azad A; School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47405 USA., Ding Y; School of Information, University of Texas at Austin, Austin, TX 78712 USA, and also with the Dell Medical School, University of Texas at Austin, Austin, TX 78712 USA., Glicksberg BS; Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA, and also with the Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA.
Jazyk:	angličtina
Zdroj:	IEEE transactions on big data [IEEE Trans Big Data] 2021 Mar; Vol. 7 (1), pp. 38-44. Date of Electronic Publication: 2020 Dec 31.
DOI:	10.1109/tbdata.2020.3048644
Abstrakt:	Traditional Machine Learning (ML) models have had limited success in predicting Coronoavirus-19 (COVID-19) outcomes using Electronic Health Record (EHR) data partially due to not effectively capturing the inter-connectivity patterns between various data modalities. In this work, we propose a novel framework that utilizes relational learning based on a heterogeneous graph model (HGM) for predicting mortality at different time windows in COVID-19 patients within the intensive care unit (ICU). We utilize the EHRs of one of the largest and most diverse patient populations across five hospitals in major health system in New York City. In our model, we use an LSTM for processing time varying patient data and apply our proposed relational learning strategy in the final output layer along with other static features. Here, we replace the traditional softmax layer with a Skip-Gram relational learning strategy to compare the similarity between a patient and outcome embedding representation. We demonstrate that the construction of a HGM can robustly learn the patterns classifying patient representations of outcomes through leveraging patterns within the embeddings of similar patients. Our experimental results show that our relational learning-based HGM model achieves higher area under the receiver operating characteristic curve (auROC) than both comparator models in all prediction time windows, with dramatic improvements to recall.
Databáze:	MEDLINE
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