Automated information extraction of key trial design elements from clinical trial publications.
Clinical trials are one of the most valuable sources of scientific evidence for improving the practice of medicine. The Trial Bank project aims to improve structured access to trial findings by including formalized trial information into a knowledge base. Manually extracting trial information from published articles is costly, but automated information extraction techniques can assist. The current study highlights a single architecture to extract a wide array of information elements from full-text publications of randomized clinical trials (RCTs). This architecture combines a text classifier with a weak regular expression matcher. We tested this two-stage architecture on 88 RCT reports from 5 leading medical journals, extracting 23 elements of key trial information such as eligibility rules, sample size, intervention, and outcome names. Results prove this to be a promising avenue to help critical appraisers, systematic reviewers, and curators quickly identify key information elements in published RCT articles.
de Bruijn
Berry
B
Institute for Information Technology, National Research Council, Ottawa, Ontario, Canada.
Carini
Simona
S
Kiritchenko
Svetlana
S
Martin
Joel
J
Sim
Ida
I
D000043:Abstracting and Indexing / Q000379:methods; D001185:Artificial Intelligence; D019317:Evidence-Based Medicine / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D038622:Internationality; D008374:Manuscripts, Medical as Topic; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D016032:Randomized Controlled Trials as Topic; D012107:Research Design