2026-09-19T05:20:22Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1230362026-06-06T01:03:18Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Roger Mark and Jesse Raffa.
Park, Joseph Seung Young.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-11-22T00:03:36Z
2019-11-22T00:03:36Z
2019
2019
https://hdl.handle.net/1721.1/123036
1127827479
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng. in Computer Science and Molecular Biology, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 85-86).
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.
by Joseph Seung Young Park
M.Eng. in Computer Science and Molecular Biology
M.Eng.inComputerScienceandMolecularBiology Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
86 pages
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Predicting intensive care unit patient outcomes through patient similarity
Thesis
application/pdf
2019-11-22T00:03:35Z
Publication
Master
EECS
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="a880867e-5d3b-43dc-8766-43a2590261fc">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Predicting intensive care unit patient outcomes through patient similarity</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2019</PublicationDate>
<Authors>
<Author>
<DisplayName>Park, Joseph Seung Young.</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>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.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>