Combining Text Classification and Hidden Markov Modeling Techniques for Structuring 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, the majority of RCT abstracts still remain unstructured. We have developed a novel automated approach to structuring RCT abstracts by combining text classification and Hidden Markov Modeling (HMM) techniques. The results (precision of 0.94, recall of 0.93) of our approach are a significant improvement over previously reported work on automated sentences categorization in RCT abstracts.

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