title: Combining text classification and Hidden Markov Modeling techniques for categorizing sentences in randomized clinical trial abstracts.
abstract: Randomized clinical trials (RCT) papers provide reliable information about efficacy of medical interventions. Current keyword based search methods to retrieve medical evidence,overload users with irrelevant information as these methods often do not take in to consideration semantics encoded within abstracts and the search query. Personalized semantic search, intelligent clinical question answering and medical evidence summarization aim to solve this information overload problem. Most of these approaches will significantly benefit if the information available in the abstracts is structured into meaningful categories (e.g., background, objective, method, result and conclusion). While many journals use structured abstract format, majority of RCT abstracts still remain unstructured.We have developed a novel automated approach to structure RCT abstracts by combining text classification and Hidden Markov Modeling(HMM) techniques. Results (precision: 0.98, recall: 0.99) of our approach significantly outperform previously reported work on automated categorization of sentences in RCT abstracts.
authors.affiliation: Biomedical Informatics Training Program, Stanford Medical Informatics, Stanford University School of Medicine, Stanford University, Stanford, CA, USA.
authors.forename: Rong
authors.initials: R
authors.lastname: Xu
authors.forename: Kaustubh
authors.initials: K
authors.lastname: Supekar
authors.forename: Yang
authors.initials: Y
authors.lastname: Huang
authors.forename: Amar
authors.initials: A
authors.lastname: Das
authors.forename: Alan
authors.initials: A
authors.lastname: Garber
mesh_terms: D000043:Abstracting and Indexing / Q000379:methods*; D008390:Markov Chains; D009323:Natural Language Processing; D016032:Randomized Controlled Trials as Topic