Evaluation of Machine Learning Algorithms for Pressure Injury Risk Assessment in a Hospital with Limited IT Resources.
Stud Health Technol Inform
; 316: 1033-1037, 2024 Aug 22.
Article
en En
| MEDLINE
| ID: mdl-39176967
ABSTRACT
Clinical decision support systems for Nursing Process (NP-CDSSs) help resolve a critical challenge in nursing decision-making through automating the Nursing Process. NP-CDSSs are more effective when they are linked to Electronic Medical Record (EMR) Data allowing for the computation of Risk Assessment Scores. Braden scale (BS) is a well-known scale used to identify the risk of Hospital-Acquired Pressure Injuries (HAPIs). While BS is widely used, its specificity for identifying high-risk patients is limited. This study develops and evaluates a Machine Learning (ML) model to predict the HAPI risk, leveraging EMR readily available data. Various ML algorithms demonstrated superior performance compared to BS (pooled model AUC/F1-score of 0.85/0.8 vs. AUC of 0.63 for BS). Integrating ML into NP-CDSSs holds promise for enhancing nursing assessments and automating risk analyses even in hospitals with limited IT resources, aiming for better patient safety.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Sistemas de Apoyo a Decisiones Clínicas
/
Úlcera por Presión
/
Registros Electrónicos de Salud
/
Aprendizaje Automático
Límite:
Humans
Idioma:
En
Revista:
Stud Health Technol Inform
Asunto de la revista:
INFORMATICA MEDICA
/
PESQUISA EM SERVICOS DE SAUDE
Año:
2024
Tipo del documento:
Article
País de afiliación:
Francia
Pais de publicación:
Países Bajos