Can structured EHR data support clinical coding? A data mining approach.
Health Syst (Basingstoke)
; 10(2): 138-161, 2020 Mar 01.
Article
en En
| MEDLINE
| ID: mdl-34104432
ABSTRACT
Structured data formats are gaining momentum in electronic health records and can be leveraged for decision support and research. Nevertheless, such structured data formats have not been explored for clinical coding, which is an essential process requiring significant manual workload in health organisations. This article explores the extent to which fully structured clinical data can support assignment of clinical codes to inpatient episodes, through a methodology that tackles high dimensionality issues, addresses the multi-label nature of coding and optimises model parameters. The methodology encompasses transformation of raw data to define a feature set, build a data matrix representation, and testing combinations of feature selection methods with machine learning models to predict code assignment. The methodology was tested with a real hospital dataset and showed varying predictive power across codes, while demonstrating the potential of leveraging structuring data to reduce workload and increase efficiency in clinical coding.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Guideline
/
Prognostic_studies
Idioma:
En
Revista:
Health Syst (Basingstoke)
Año:
2020
Tipo del documento:
Article
País de afiliación:
Portugal