{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/119530"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/119530","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Automating comprehensive literature review of cancer risk journal articles","abstract":"Recently, researchers at the Massachusetts General Hospital and the Dana Farber Cancer Institute built a calculator that estimates risk of cancer based on genetic mutations. The calculator requires a lot of high quality data from medical journals, which is laborious to obtain by hand. In this thesis, I automate the process of obtaining medical abstracts from PubMed and develop a classifier that uses domain knowledge to determine relevant abstracts. The classifier is very accurate (percent correct = 0.898, F1 = 0.86, recall = 0.905), and is significantly better than the majority baseline. I explore an alternative model that exploits rationales within abstracts, which could lead to an even greater accuracy. After determining relevant abstracts, it's useful to find the size of the cohorts, which is an indicator for the quality of the medical study. Hence, I built a classifier that can accurately extract cohort sizes from abstracts (F1 = 0.883), and developed a strong baseline for distinguishing gene carrier cohort sizes from noncarriers.","abstract_html":"Recently, researchers at the Massachusetts General Hospital and the Dana Farber Cancer Institute built a calculator that estimates risk of cancer based on genetic mutations. The calculator requires a lot of high quality data from medical journals, which is laborious to obtain by hand. In this thesis, I automate the process of obtaining medical abstracts from PubMed and develop a classifier that uses domain knowledge to determine relevant abstracts. The classifier is very accurate (percent correct = 0.898, F1 = 0.86, recall = 0.905), and is significantly better than the majority baseline. I explore an alternative model that exploits rationales within abstracts, which could lead to an even greater accuracy. After determining relevant abstracts, it's useful to find the size of the cohorts, which is an indicator for the quality of the medical study. Hence, I built a classifier that can accurately extract cohort sizes from abstracts (F1 = 0.883), and developed a strong baseline for distinguishing gene carrier cohort sizes from noncarriers.","abstract_has_math":false,"creators":["Kim, Heeyoon"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Regina Barzilay."],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017","date_published":"2017","updated_at":"2026-07-22T22:22:10Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/119530","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Regina Barzilay."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. 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They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/119530"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.","This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (page 51)."]},{"key":"dc:description.abstract","label":"Abstract","values":["Recently, researchers at the Massachusetts General Hospital and the Dana Farber Cancer Institute built a calculator that estimates risk of cancer based on genetic mutations. The calculator requires a lot of high quality data from medical journals, which is laborious to obtain by hand. In this thesis, I automate the process of obtaining medical abstracts from PubMed and develop a classifier that uses domain knowledge to determine relevant abstracts. The classifier is very accurate (percent correct = 0.898, F1 = 0.86, recall = 0.905), and is significantly better than the majority baseline. I explore an alternative model that exploits rationales within abstracts, which could lead to an even greater accuracy. After determining relevant abstracts, it's useful to find the size of the cohorts, which is an indicator for the quality of the medical study. Hence, I built a classifier that can accurately extract cohort sizes from abstracts (F1 = 0.883), and developed a strong baseline for distinguishing gene carrier cohort sizes from noncarriers."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["Automating comprehensive literature review of cancer risk journal articles"]}]}],"canonical_facts":{"dc:contributor.advisor":["Regina Barzilay."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. 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In this thesis, I automate the process of obtaining medical abstracts from PubMed and develop a classifier that uses domain knowledge to determine relevant abstracts. The classifier is very accurate (percent correct = 0.898, F1 = 0.86, recall = 0.905), and is significantly better than the majority baseline. I explore an alternative model that exploits rationales within abstracts, which could lead to an even greater accuracy. After determining relevant abstracts, it's useful to find the size of the cohorts, which is an indicator for the quality of the medical study. Hence, I built a classifier that can accurately extract cohort sizes from abstracts (F1 = 0.883), and developed a strong baseline for distinguishing gene carrier cohort sizes from noncarriers."],"dc:description.degree":["M. Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/119530"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Automating comprehensive literature review of cancer risk journal articles"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:22:10Z"}