Automating comprehensive literature review of cancer risk journal articles

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.

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

2017

Author dc:creator

• Kim, Heeyoon

Advisor dc:contributor.advisor

• Regina Barzilay.

• Electrical Engineering and Computer Science.

Statement 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/119530

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/119530