Automatic identification of comparative effectiveness research from medline citations to support clinicians' treatment information needs.

UNLABELLED: Online knowledge resources such as Medline can address most clinicians' patient care information needs. Yet, significant barriers, notably lack of time, limit the use of these sources at the point of care. The most common information needs raised by clinicians are treatment-related. Comparative effectiveness studies allow clinicians to consider multiple treatment alternatives for a particular problem. Still, solutions are needed to enable efficient and effective consumption of comparative effectiveness research at the point of care.
OBJECTIVE: Design and assess an algorithm for automatically identifying comparative effectiveness studies and extracting the interventions investigated in these studies.
METHODS: The algorithm combines semantic natural language processing, Medline citation metadata, and machine learning techniques. We assessed the algorithm in a case study of treatment alternatives for depression.
RESULTS: Both precision and recall for identifying comparative studies was 0.83. A total of 86% of the interventions extracted perfectly or partially matched the gold standard.
CONCLUSION: Overall, the algorithm achieved reasonable performance. The method provides building blocks for the automatic summarization of comparative effectiveness research to inform point of care decision-making.