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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.

Authors:

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

Braja Patra, University of Texas Health Science Center at Houston

Presentation Materials:

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