Abstract
Lack of standardized representation of natural language processing (NLP) components in phenotyping algorithms hinders portability of the phenotyping algorithms and their execution in a high-throughput and reproducible manner. The objective of the study is to develop and evaluate a standard-driven approach - CQL4NLP - that integrates a collection of NLP extensions represented in the HL7 Fast Healthcare Interoperability Resources (FHIR) standard into the clinical quality language (CQL). A minimal NLP data model with 11 NLP-specific data elements was created, including six FHIR NLP extensions. All 11 data elements were identified from their usage in real-world phenotyping algorithms. An NLP ruleset generation mechanism was integrated into the NLP2FHIR pipeline and the NLP rulesets enabled comparable performance for a case study with the identification of obesity comorbidities. The NLP ruleset generation mechanism created a reproducible process for defining the NLP components of a phenotyping algorithm and its execution.
Original language | English (US) |
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Pages (from-to) | 624-633 |
Number of pages | 10 |
Journal | AMIA ... Annual Symposium proceedings. AMIA Symposium |
Volume | 2021 |
State | Published - 2021 |
ASJC Scopus subject areas
- General Medicine