2026-09-18T22:01:18Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1391322022-01-15T03:13:28Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Szolovits, Peter
Berlin, Heather
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2022-01-14T14:51:50Z
2022-01-14T14:51:50Z
2021-06
2021-06-24T19:15:19.880Z
https://hdl.handle.net/1721.1/139132
Approximately 3 million patients in the US have been diagnosed with Ulcerative Colitis, a chronic inflammatory disease affecting the colon. Uncovering patient subgroups could improve treatment guidelines and help physicians choose an appropriate treatment plan for a patient. Here, we outline a Python implementation to generate a cohort from a dataset in the OMOP Common Data Model (CDM), propose a patient timeline visualization tool, create and analyze a cohort of Ulcerative Colitis patients using a claims dataset. We extract patient features and use dimensionality reduction techniques along with clustering to identify patient subgroups. We observe four patient subgroups consisting of distinct patient characteristics, most prominently age, insurance type, sex, and type of initial conventional therapy prescription.
S.M.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
Subgrouping Ulcerative Colitis Patients using Administrative Claims Data
Thesis
application/pdf
Master
Master of Science in Electrical Engineering and Computer Science
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="c43e6be1-b382-47cd-9f7a-46af09ec8845">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Title>Subgrouping Ulcerative Colitis Patients using Administrative Claims Data</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2021-06</PublicationDate>
<Authors>
<Author>
<DisplayName>Berlin, Heather</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://rightsstatements.org/page/InC-EDU/1.0/</License>
<Abstract>Approximately 3 million patients in the US have been diagnosed with Ulcerative Colitis, a chronic inflammatory disease affecting the colon. Uncovering patient subgroups could improve treatment guidelines and help physicians choose an appropriate treatment plan for a patient. Here, we outline a Python implementation to generate a cohort from a dataset in the OMOP Common Data Model (CDM), propose a patient timeline visualization tool, create and analyze a cohort of Ulcerative Colitis patients using a claims dataset. We extract patient features and use dimensionality reduction techniques along with clustering to identify patient subgroups. We observe four patient subgroups consisting of distinct patient characteristics, most prominently age, insurance type, sex, and type of initial conventional therapy prescription.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>