The classification algorithms to support the management of the patient with femur fracture.
Biomedical data analysis
Classification algorithms
Femur fracture
Machine learning
Modelling
Journal
BMC medical research methodology
ISSN: 1471-2288
Titre abrégé: BMC Med Res Methodol
Pays: England
ID NLM: 100968545
Informations de publication
Date de publication:
16 Jul 2024
16 Jul 2024
Historique:
received:
11
08
2023
accepted:
05
07
2024
medline:
17
7
2024
pubmed:
17
7
2024
entrez:
16
7
2024
Statut:
epublish
Résumé
Effectiveness in health care is a specific characteristic of each intervention and outcome evaluated. Especially with regard to surgical interventions, organization, structure and processes play a key role in determining this parameter. In addition, health care services by definition operate in a context of limited resources, so rationalization of service organization becomes the primary goal for health care management. This aspect becomes even more relevant for those surgical services for which there are high volumes. Therefore, in order to support and optimize the management of patients undergoing surgical procedures, the data analysis could play a significant role. To this end, in this study used different classification algorithms for characterizing the process of patients undergoing surgery for a femoral neck fracture. The models showed significant accuracy with values of 81%, and parameters such as Anaemia and Gender proved to be determined risk factors for the patient's length of stay. The predictive power of the implemented model is assessed and discussed in view of its capability to support the management and optimisation of the hospitalisation process for femoral neck fracture, and is compared with different model in order to identify the most promising algorithms. In the end, the support of artificial intelligence algorithms laying the basis for building more accurate decision-support tools for healthcare practitioners.
Identifiants
pubmed: 39014322
doi: 10.1186/s12874-024-02276-5
pii: 10.1186/s12874-024-02276-5
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
150Informations de copyright
© 2024. The Author(s).
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