Representing Clinical Diagnostic Criteria in Quality Data Model Using Natural Language Processing

摘要

Constructing standard and computable clinical diagnostic criteria is an important and challenging research area in clinical informatics community. In this study, we present our framework and methods for representing clinical diagnostic criteria in Quality Data Model (QDM) using natural language processing (NLP) technologies. We used a clinical NLP tool known as cTAKES for preprocessing of textual diagnostic criteria. We created mappings between cTAKES type system and QDM elements in both datatype and data levels. We evaluated the performance of our NLP-based approach by annotating 218 individual diagnostic criteria in the categories of Symptom and Laboratory Test. In conclusion, our NLP-based approach is a feasible solution in developing diagnostic criteria representation and computerization.