2026-09-18T22:34:55Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1528262023-11-03T03:18:03Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Ghassemi, Marzyeh
Jin, Qixuan
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2023-11-02T20:19:16Z
2023-11-02T20:19:16Z
2023-09
2023-09-21T14:26:13.611Z
https://hdl.handle.net/1721.1/152826
Given the complexity of trauma presentations, particularly in those involving multiple areas of the body, overlooked injuries are common during the initial assessment by a clinician. We are motivated to develop an automated trauma pattern discovery framework for comprehensive identification of injury patterns which may eventually support diagnostic decision-making. We analyze 1,162,399 patients from the Trauma Quality Improvement Program with a disentangled variational autoencoder, weakly supervised by a latent-space classifier of auxiliary features. We also develop a novel scoring metric that serves as a proxy for clinical intuition in extracting clusters with clinically meaningful injury patterns. We validate the extracted clusters with clinical experts, and explore the patient characteristics of selected groupings. Our metric is able to perform model selection and effectively filter clusters for clinically-validated relevance.
S.M.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
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https://rightsstatements.org/page/InC-EDU/1.0/
Weakly Supervised Representation Learning for Trauma
Injury Pattern Discovery
Thesis
application/pdf
Master
Master of Science in Electrical Engineering and Computer Science
Publication
unknown
unknown
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Injury Pattern Discovery</Title>
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<Abstract>Given the complexity of trauma presentations, particularly in those involving multiple areas of the body, overlooked injuries are common during the initial assessment by a clinician. We are motivated to develop an automated trauma pattern discovery framework for comprehensive identification of injury patterns which may eventually support diagnostic decision-making. We analyze 1,162,399 patients from the Trauma Quality Improvement Program with a disentangled variational autoencoder, weakly supervised by a latent-space classifier of auxiliary features. We also develop a novel scoring metric that serves as a proxy for clinical intuition in extracting clusters with clinically meaningful injury patterns. We validate the extracted clusters with clinical experts, and explore the patient characteristics of selected groupings. Our metric is able to perform model selection and effectively filter clusters for clinically-validated relevance.</Abstract>
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