Building and Evaluating Cancer Prescreening Models with Electronic Health Records

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Saowakon, Pasapol

Advisor dc:contributor.advisor

• Rinard, Martin C.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/151407

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/151407