Predicting intensive care unit patient outcomes through patient similarity

An ICU stay involves invasive treatments, and frequently, the decision to continue therapy is made with limited information based on the physician's personal experience. This thesis proposal describes a tool to assist this decision by identifying similar patients and using their outcomes for prediction. We used the eICU Collaborative Research Database (eICU-CRD) v2.0 for the project. Different time varying and time constant features about the patient's demographics and clinical trajectory was used as input data, such as patient age and longitudinal blood pressure measurement. Using this information, a Cox Proportional Hazards model was built to map the multivariate time series of input data to a univariate time series, which was used to match the patient to a cohort of similar patients. Based on the cohort, this model predicted the probability of a healthy discharge by using the aggregate outcome of the cohort for prediction.

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

2019

Author dc:creator

• Park, Joseph Seung Young.

Advisor dc:contributor.advisor

• Roger Mark and Jesse Raffa.

• 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/1721.1/123036

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123036