EHR Question Answering
The last decade has seen widespread adoption of electronic health records (EHRs) across hospitals and clinics in the US. Physicians frequently seek answers to questions from a patient’s EHR to support clinical decision-making.
It is not too hard to imagine a future where a physician interacts with an EHR system and asks it complex questions and expects precise answers with adequate context from a patient’s record. Central to such a world is a medical question answering system that processes natural language questions asked by physicians and finds answers to the questions from all sources in a patient’s record.
I will talk about the steps we have taken towards building such a system in terms of (1) creating a large-scale dataset emrQA with over 1M questions, logical forms (that capture information/answering needs in a structured format), and answers in clinical notes, (2) building a model for semantic parsing to map questions to logical forms and (3) building an automatic medical question answering system that answers these questions.
I will also discuss the challenges and roadblocks and lay out a vision for how we can make further progress in realizing such a future.
Senior NLP Research Scientist at MIT-IBM Watson AI Lab
Preethi Raghavan is a Research Staff Member at IBM Research Cambridge and a member of the MIT-IBM Watson AI Lab working on various problems in natural language processing.
She has been working on automatic information extraction methods from medical text for the past 10 years.
The current focus of her work is patient-specific question answering from electronic health records to help physicians with clinical decision making.