Prediction of candidemia with machine learning techniques: state of the art.
Future Microbiol
; 19(10): 931-940, 2024 Jul 02.
Article
en En
| MEDLINE
| ID: mdl-39072500
ABSTRACT
In this narrative review, we discuss studies assessing the use of machine learning (ML) models for the early diagnosis of candidemia, focusing on employed models and the related implications. There are currently few studies evaluating ML techniques for the early diagnosis of candidemia as a prediction task based on clinical and laboratory features. The use of ML tools holds promise to provide highly accurate and real-time support to clinicians for relevant therapeutic decisions at the bedside of patients with suspected candidemia. However, further research is needed in terms of sample size, data quality, recognition of biases and interpretation of model outputs by clinicians to better understand if and how these techniques could be safely adopted in daily clinical practice.
Candida is a type of fungus that can cause fatal infections. To confirm the presence of the infection, doctors may search for the fungus in the blood. Here, we discuss if computer systems can help to identify infection more easily and more rapidly.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Candidemia
/
Aprendizaje Automático
Límite:
Humans
Idioma:
En
Año:
2024
Tipo del documento:
Article