CQL4NLP: Development and Integration of FHIR NLP Extensions in Clinical Quality Language for EHR-driven Phenotyping.
AMIA Jt Summits Transl Sci Proc
; 2021: 624-633, 2021.
Article
en En
| MEDLINE
| ID: mdl-34457178
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.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Procesamiento de Lenguaje Natural
/
Registros Electrónicos de Salud
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
AMIA Jt Summits Transl Sci Proc
Año:
2021
Tipo del documento:
Article
País de afiliación:
Mongolia