CQL4NLP: Development and Integration of FHIR NLP Extensions in Clinical Quality Language for EHR-driven Phenotyping

Andrew Wen, Luke V. Rasmussen, Daniel Stone, Sijia Liu, Rick Kiefer, Prakash Adekkanattu, Pascal S. Brandt, Jennifer A. Pacheco, Yuan Luo, Fei Wang, Jyotishman Pathak, Hongfang Liu, Guoqian Jiang

Research output: Contribution to journalArticlepeer-review

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 languageEnglish (US)
Pages (from-to)624-633
Number of pages10
JournalAMIA ... Annual Symposium proceedings. AMIA Symposium
Volume2021
StatePublished - 2021

ASJC Scopus subject areas

  • Medicine(all)

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