MANUSCRIPT: Natural language processing: algorithms and tools to extract computable information from EHRs and from the biomedical literature
The increasing adoption of electronic health records EHRs and the corresponding interest in using these data for quality improvement and research have made it clear that the interpretation of narrative text contained in the records is a critical step. The biomedical literature is another important information source that can benefit from approaches requiring structuring of data contained in narrative text. For the first time, we dedicate an entire issue of JAMIA to biomedical natural language processing NLP, a topic that has been among the most cited in this journal for the past few years. We start with a description of a contest to select the best performing algorithms for detection of temporal relationships in clinical documents see page 806, followed by a general review of significance and brief description of commonly used methods to address this task see page 814.
Written by Brian S McGowan, PhD
Brian is a research scientist and educational technologist. He helped transform Pfizer’s Medical Education Group and previously served in educational leadership roles at HealthAnswers, Inc.; Acumentis, LLC.; Cephalon; and Wyeth. He taught graduate medical education programs at Arcadia University for 10 years. Dr. McGowan recently authored the book "#socialQI: Simple Solutions for Improving Your Healthcare" and has been invited to speak internationally on the subject of information flow, technology, and learning in healthcare.
