Computer-Assisted Diagnostic Coding: Effectiveness of an NLP-based approach using SNOMED CT to ICD-10 mappings.
AMIA Annu Symp Proc
; 2018: 807-816, 2018.
Article
em En
| MEDLINE
| ID: mdl-30815123
ABSTRACT
Computer-assisted (diagnostic) coding (CAC) aims to improve the operational productivity and accuracy of clinical coders. The level of accuracy, especially for a wide range of complex and less prevalent clinical cases, remains an open research problem. This study investigates this problem on a broad spectrum of diagnostic codes and, in particular, investigates the effectiveness of utilising SNOMED CT for ICD-10 diagnosis coding. Hospital progress notes were used to provide the narrative rich electronic patient records for the investigation. A natural language processing (NLP) approach using mappings between SNOMED CT and ICD-10-AM (Australian Modification) was used to guide the coding. The proposed approach achieved 54.1% sensitivity and 70.2% positive predictive value. Given the complexity of the task, this was encouraging given the simplicity of the approach and what was projected as possible from a manual diagnosis code validation study (76.3% sensitivity). The results show the potential for advanced NLP-based approaches that leverage SNOMED CT to ICD-10 mapping for hospital in-patient coding.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Linguagem Natural
/
Classificação Internacional de Doenças
/
Systematized Nomenclature of Medicine
/
Codificação Clínica
Tipo de estudo:
Diagnostic_studies
/
Guideline
/
Prognostic_studies
Limite:
Humans
País/Região como assunto:
Oceania
Idioma:
En
Revista:
AMIA Annu Symp Proc
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2018
Tipo de documento:
Article
País de afiliação:
Austrália