Extracting subject demographic information from abstracts of randomized clinical trial reports.
In order to make more informed healthcare decisions, consumers need information systems that deliver accurate and reliable information about their illnesses and potential treatments. Reports of randomized clinical trials (RCTs) provide reliable medical evidence about the efficacy of treatments. Current methods to access, search for, and retrieve RCTs are keyword-based, time-consuming, and suffer from poor precision. Personalized semantic search and medical evidence summarization aim to solve this problem. The performance of these approaches may improve if they have access to study subject descriptors (e.g. age, gender, and ethnicity), trial sizes, and diseases/symptoms studied. We have developed a novel method to automatically extract such subject demographic information from RCT abstracts. We used text classification augmented with a Hidden Markov Model to identify sentences containing subject demographics, and subsequently these sentences were parsed using Natural Language Processing techniques to extract relevant information. Our results show accuracy levels of 82.5%, 92.5%, and 92.0% for extraction of subject descriptors, trial sizes, and diseases/symptoms descriptors respectively.
Xu
Rong
R
Biomedical Informatics Training Program, Stanford University School of Medicine, USA.
Garten
Yael
Y
Supekar
Kaustubh S
KS
Das
Amar K
AK
Altman
Russ B
RB
Garber
Alan M
AM
D000043:Abstracting and Indexing; D016247:Information Storage and Retrieval / Q000379:methods*; D008390:Markov Chains; D009323:Natural Language Processing; D016032:Randomized Controlled Trials as Topic