Abstract: This paper presents a method for converting natural language questions about structured data in the electronic health record (EHR) into logical forms. The logical forms can then subsequently be converted to EHR-dependent structured queries. The natural language processing task, known as semantic parsing, has the potential to convert questions to logical forms with extremely high precision, resulting in a system that is usable and trusted by clinicians for real-time use in clinical settings. We propose a hybrid semantic parsing method, combining rule-based methods with a machine learning-based classifier. The overall semantic parsing precision on a set of 446 questions is 95.6%. The parser's rules furthermore allow it to ``know what it does not know'', enabling the system to indicate when unknown terms prevent it from understanding the question's full logical structure. When combined with a module for converting a logical form into an EHR-dependent query, this high-precision approach allows for a question answering system to provide a user with a single, verifiably correct answer.

Learning Objective 1: Understand how clinical questions about patient-specific structured data can be understood using natural language processing.


Kirk Roberts (Presenter)
University of Texas Health Science Center at Houston

Braja Patra, University of Texas Health Science Center at Houston

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