Weakly Supervised Representation Learning for Trauma
Injury Pattern Discovery
Name
jin-qixuanj-sm-eecs-2023-thesis.pdf
Description
Thesis PDF
Size
17.58 MB
Format
Adobe PDF
Checksum (MD5)
d40dcaba8c53a2d301f5fae1982a4919
Author(s)
Jin, Qixuan
Advisor(s)
Ghassemi, Marzyeh
Date Issued
September 2023
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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