Towards a personalized health care using a divisive hierarchical clustering approach for comorbidity and the prediction of conditioned group risks.
Health Informatics J
; 29(4): 14604582231212494, 2023.
Article
en En
| MEDLINE
| ID: mdl-38072502
ABSTRACT
The objective was to assess risk of hospitalization and mortality of comorbidities using divisive hierarchical risk clustering to advice clinical interventions. Subjects and Methods:
Data from the EHR of a general population, 3799885 adults, followed by 5 years. Model were performed using Spark and Scikit-learn and accuracy for the models was analyzed.Results:
The number of models generated depends in part on the number of chronic diseases included (ex testing a sample of six diseases, a total number of 397 models for all-cause mortality and 431 models for hospitalization). The estimated models offered an ordered selection for the relevant clinical variables and their estimated risk as a group and for the individual patient in the group. Accuracy was assessed according to age, sex and the cardinality of the comorbid groups. A mobile version and dashboard were developed.Conclusion:
The software developed stratified hospital admission and mortality risk in clusters of chronic diseases, and for a given patient, it could advise intensifying treatment or reallocating the patient risk.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Atención a la Salud
/
Hospitalización
Límite:
Adult
/
Humans
Idioma:
En
Revista:
Health Informatics J
Año:
2023
Tipo del documento:
Article
País de afiliación:
España