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. INTRODUCTION Randomized clinical trial (RCT) papers are a critical resource for the biomedical community’s ability to get reliable information on the efficacy of medical interventions. Clinicians, researchers and patients, currently use search engines such as PubMed and Google to retrieve RCT papers of interest. These search engines employ keyword-based techniques to find relevant papers. More often than not these techniques have poor precision, mostly because they fail to consider semantics encoded in the paper as well as the query. The use of MeSH subheadings to index abstracts in PubMed aims to capture the semantics of the abstract. However, it fails to capture the context of the user query, and the semantics captured by MeSH subheadings are limited. Personalized semantic search, intelligent clinical question answering and medical evidence summarization are approaches that aim to improve the precision of the search results [1, 2]. In the healthcare setting, these approaches entail extracting structured and machine understandable information from papers and patient records. Recent work for the RCT Bank [3] has focused on modeling information that is to be extracted from RCT papers. This model serves as basis of a structured query interface to retrieve relevant information from RCT papers that are encoded as instances of the RCT Schema. Currently, this information is manually extracted from RCT papers, and is archived in the RCT Bank. To provide personalized medical evidence from RCT papers, the PICO framework [4] advocates an automated approach to extracting structured information from RCT papers and patient records that is related to problem, population, intervention, comparison, and outcome. The structured information extracted from the patient records serves as a query context, and is matched with the structured information captured from the RCT paper. However, the PICO framework only provides a task-based model of the clinical information-seeking process; no operational methods are provided in the framework to automatically extract structured information. In an RCT abstract, the sentence type information (e.g., background, objective, method, result and conclusion) as well as sentence content, is useful to automatically extract information to populate either the PICO model or the RCT Schema. For example, patient population information is more likely contained in sentences in method section, sentences in objective section are more likely to provide information about intervention and comparison, and outcome is usually contained in the result and conclusion sections. Structured abstracts, introduced in 1987, explicitly provide type information for all the sentences in an abstract. While many journals use structured abstract