A corpus-driven standardization framework for encoding clinical problems with HL7 FHIR

Abstract

Free-text problem descriptions are brief explanations of patient diagnoses and issues, commonly found in problem lists and other prominent areas of the medical record. These compact representations often express complex and nuanced medical conditions, making their semantics challenging to fully capture and standardize. In this study, we describe a framework for transforming free-text problem descriptions into standardized Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) models. This approach leverages a combination of domain-specific dependency parsers, Bidirectional Encoder Representations from Transformers (BERT) natural language models, and cui2vec Unified Medical Language System (UMLS) concept vectors to align extracted concepts from free-text problem descriptions into structured FHIR models. A neural network classification model is used to classify thirteen relationship types between concepts, facilitating mapping to the FHIR Condition resource. We use data programming, a weak supervision approach, to eliminate the need for a manually annotated training corpus. Shapley values, a mechanism to quantify contribution, are used to interpret the impact of model features. We found that our methods identified the focus concept, or primary clinical concern of the problem description, with an F₁ score of 0.95. Relationships from the focus to other modifying concepts were extracted with an F₁ score of 0.90. When classifying relationships, our model achieved a 0.89 weighted average F₁ score, enabling accurate mapping of attributes into HL7 FHIR models. We also found that the BERT input representation predominantly contributed to the classifier decision as shown by the Shapley values analysis.

Keywords: Deep Learning (D000077321); Health Information Interoperability (D000073892); Natural Language Processing (D009323); Semantics (D012660); Systematized Nomenclature of Medicine (D039061).