Physicians treating patients in the clinic, on the floor, or in the emergency room are faced with an overwhelming amount of complex information about their patients, with little time to review it. HARVEST is an interactive patient record summarization system, which aims to support physicians in their information workflow. It extracts content from the patient notes, where key clinical information resides, aggregates and presents information through time. HARVEST is currently deployed at NewYork-Presbyterian hospital. It relies on a distributed platform for processing data as they get pushed into the electronic health record. We are now investigating summarization models of patient records that identify their co-morbidities and their status through time, by modeling all observations in the record, from the notes to laboratory test measurements and other structured information like billing codes. This project is a collaboration between Dr. Noémie Elhadad in Biomedical Informatics, Dr. Chris Wiggins in Applied Physics and Applied Mathematics, and NewYork-Presbyterian hospital.
My research is in biomedical informatics, natural language processing, and data mining. I develop techniques that aim to support clinicians, patients, and health researchers in their information workflow by automatically extracting and making accessible information from unstructured, large clinical datasets (e.g., the electronic patient record) and patient platforms (e.g., online health communities).