{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/151407"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/151407","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Building and Evaluating Cancer Prescreening Models with Electronic Health Records","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Saowakon, Pasapol"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Rinard, Martin C."],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-06","date_published":"2023-06","updated_at":"2026-07-22T22:21:31Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"rights_urls":["https://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/151407","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Rinard, Martin C."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Building and Evaluating Cancer Prescreening Models with Electronic Health Records"]}]}],"canonical_facts":{"dc:contributor.advisor":["Rinard, Martin C."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Saowakon, Pasapol"],"dc:date.accessioned":["2023-07-31T19:37:19Z"],"dc:date.available":["2023-07-31T19:37:19Z"],"dc:date.issued":["2023-06"],"dc:description.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."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/151407"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"dc:rights.uri":["https://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Building and Evaluating Cancer Prescreening Models with Electronic Health Records"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Engineering in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:21:31Z"}