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oai:dspace.mit.edu:1721.1/1390722022-01-15T03:32:12Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

Guttag, John Syed, Zeeshan Saeed, Mohammed Yang, Alexander Y. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2022-01-14T14:48:14Z 2022-01-14T14:48:14Z 2021-06 2021-06-17T20:15:01.000Z https://hdl.handle.net/1721.1/139072 The Sequential Organ Failure Assessment (SOFA) score is a scoring system useful for predicting clinical outcomes in the intensive care unit, such as organ failure and mortality. The availability of increasingly detailed electronic clinical data has allowed for the creation of more powerful models to better predict and improve patient outcomes. However, there is little work in predicting the underlying physiological measurements that define the SOFA score. In this paper, we consider predicting changes to the individual components of the SOFA score. We use multi-task learning frameworks to predict future values for the SOFA score components, with the goal of sharing information across the different tasks to improve overall predictive performance. We use approximately 53,000 days of time-series Electronic Health Record (EHR) data taken from 10,000 ICU stays in the Multiparameter Intelligent Monitoring in Intensive Care IV (MIMIC-IV) dataset. Evaluating on a test holdout set of 10% of our data, we compare performance of our multi-task learning models to individually-trained deep networks that predict each component without parameter sharing. Model performance suggests that there is a small advantage to multi-task learning over unregularized networks, but no advantage compared to networks that employ regularization. M.Eng. Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ Predicting Individual Components of the SOFA Score using Multi-Task Learning Thesis application/pdf Master Master of Engineering in Electrical Engineering and Computer Science Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="8348ef8e-2a5d-4463-9588-d14be7bc6421"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>Predicting Individual Components of the SOFA Score using Multi-Task Learning</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2021-06</PublicationDate> <Authors> <Author> <DisplayName>Yang, Alexander Y.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>https://rightsstatements.org/page/InC-EDU/1.0/</License> <Abstract>The Sequential Organ Failure Assessment (SOFA) score is a scoring system useful for predicting clinical outcomes in the intensive care unit, such as organ failure and mortality. The availability of increasingly detailed electronic clinical data has allowed for the creation of more powerful models to better predict and improve patient outcomes. However, there is little work in predicting the underlying physiological measurements that define the SOFA score. In this paper, we consider predicting changes to the individual components of the SOFA score. We use multi-task learning frameworks to predict future values for the SOFA score components, with the goal of sharing information across the different tasks to improve overall predictive performance. We use approximately 53,000 days of time-series Electronic Health Record (EHR) data taken from 10,000 ICU stays in the Multiparameter Intelligent Monitoring in Intensive Care IV (MIMIC-IV) dataset. Evaluating on a test holdout set of 10% of our data, we compare performance of our multi-task learning models to individually-trained deep networks that predict each component without parameter sharing. Model performance suggests that there is a small advantage to multi-task learning over unregularized networks, but no advantage compared to networks that employ regularization.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>