Building and Evaluating Cancer Prescreening
Models with Electronic Health Records
Name
saowakon-zoomswk-meng-eecs-2023-thesis.pdf
Description
Thesis PDF
Size
1.21 MB
Format
Adobe PDF
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afcd7858ea18ad900981aca001ade7c0
Author(s)
Saowakon, Pasapol
Advisor(s)
Rinard, Martin C.
Date Issued
June 2023
Publisher
Massachusetts Institute of Technology
Abstract
Cancer is a leading cause of death that kills over ten million people every year, and many times delayed treatment is the culprit. Building on a recent framework, we used electronic health records from TriNetX to develop prescreening models for ten different cancer types: biliary tract, brain, breast (female), colon, esophageal, gastric, kidney, liver, lung, and ovarian. The models showed great performance, with neural network models consistently but marginally outperforming their logistic regression counterparts. As expected, we found that models trained to detect specific cancer types performed noticeably better than ones trained more generally to detect any cancer. All models proved to be reasonably robust in geographical, racial, and temporal external validations, although a prospective study is still needed to verify the performance and the potential impact of our models.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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