Unveiling Fall Risk Factors: Nurse-Driven Corpus Development for Natural Language Processing.
Stud Health Technol Inform
; 315: 373-378, 2024 Jul 24.
Article
en En
| MEDLINE
| ID: mdl-39049286
ABSTRACT
Hospital-acquired falls are a continuing clinical concern. The emergence of advanced analytical methods, including NLP, has created opportunities to leverage nurse-generated data, such as clinical notes, to better address the problem of falls. In this nurse-driven study, we employed an iterative process for expert manual annotation of RNs clinical notes to enable the training and testing of an NLP pipeline to extract factors related to falls. The resulting annotated data corpus had moderately high interrater reliability (F-score=0.74) and captured a breadth of clinical concepts for extraction with potential utility beyond patient falls. Further research is needed to determine which annotation tasks most benefit from nursing expert annotators, to optimize efficiency when tapping into the invaluable resource represented by the nursing workforce.
Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Accidentes por Caídas
/
Procesamiento de Lenguaje Natural
/
Registros Electrónicos de Salud
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:
Estados Unidos