Automatically Extracting Sentences from Medline Citations to Support Clinicians' Information Needs

Abstract

Online health knowledge resources contain answers to most of the information needs raised by clinicians in the course of care. However, significant barriers limit the use of these resources for decision-making, especially clinicians' lack of time. Existing solutions are less optimal when information needs cannot be met without substantial cognitive effort and time. Objectives: In this study, we assessed the feasibility of automatically generating knowledge summaries for a particular clinical topic composed of relevant sentences extracted from Medline citations. Methods: The proposed approach combines information retrieval and semantic information extraction techniques to identify relevant sentences from Medline abstracts. We assessed this approach in two case studies on the treatment alternatives for depression and Alzheimer's disease. Results: A total of 515 out of 564 (91.3%) sentences retrieved in the two case studies were relevant to the topic of interest. About one third of the relevant sentences described factual knowledge or a study conclusion that can be used for supporting information needs at the point of care. Conclusions: Our proposed technical approach to helping clinicians meet their information needs is promising. The approach can be extended for other knowledge resources and information need types.