Generalizable neural network representations of patient state in the intensive care unit

Understanding changes in physiology in patients in the Intensive Care Unit (ICU) is important in determining care decisions. Machine learning algorithms have been used to model patient physiology to predict patient outcomes and administration of interventions. These predictions can be made directly on the raw patient data extracted from electronic health records. However, this data can be high dimensional with extraneous information. Neural networks, and in particular, autoencoders and sequence-to-sequence models, can be used to extract the important attributes of this time-series data without manual feature selection. In this work, we explore how learned encoded representations of physiological time-series and events time-series can be used to effectively predict outcomes on a variety of tasks. We compare the representations extracted from sequence-to-sequence models with representations extracted from autoencoders. We evaluate these representations on the task of predicting patient mortality and first onset of ventilator and vasopressor interventions. Our best representations achieve AUCs of 0.83, 0.91, and 0.91 on the tasks of mortality prediction, vasopressor first onset prediction, and ventilator first onset prediction, which is comparable to the performances using the raw features.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Gong, Maryann M.

Advisor dc:contributor.advisor

• John Guttag and Jen Gong.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/122909

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122909