Description
This paper describes a novel technique for annotating logical forms and answers for clinical questions by utilizing Fast Healthcare Interoperability Resources (FHIR). Such annotations are widely used in building the semantic parsing models (which aim at understanding the precise meaning of natural language questions by converting them to machine-understandable logical forms). These systems focus on reducing the time it takes for a user to get to information present in electronic health records (EHRs). Directly annotating questions with logical forms is a challenging task and involves a time-consuming step of concept normalization annotation. We aim to automate this step using the normalized codes present in a FHIR resource. Using the proposed approach, two annotators curated an annotated dataset of 1000 questions in less than 1 week. To assess the quality of these annotations, we trained a semantic parsing model which achieved an accuracy of 94.2% on this corpus.
Learning Objective: Will be able to describe the process of annotating logical forms and answers using fast healthcare interoperability resources (FHIR).
Authors:
Sarvesh Soni (Presenter)
The University of Texas Health Science Center at Houston
Meghana Gudala, The University of Texas Health Science Center at Houston
Daisy Wang, University of Florida
Kirk Roberts, The University of Texas Health Science Center at Houston
Presentation Materials: