Extracting Semantics from Clinical Text

There is a great amount of information captured in physicians’ comments made during health care. Increasingly, researchers are finding valuable uses by mining and aggregating this data in clinical and translational studies which lead to improved patient care. However, most patient information that describes patient state, diagnostic procedures, and disease progress is represented in free-text form. For meaningful use, one of the challenges is to capture the rich semantics surrounding the medical concepts in partially structured clinical text. In this talk, I will first summarize my past research on statistical section segmentation to extract the structure of clinical notes and assertion analysis to capture the semantics surrounding the medical concepts. I will present clinical application examples where these more sophisticated semantic representation approaches were proven to be more effective compared to the baseline bag-of-words approach.  I will conclude with my ongoing research on clinical event extraction with change-of-state and my future research directions.