Decision support through risk cost estimation in 30-day hospital unplanned readmission.
PLoS One
; 17(7): e0271331, 2022.
Article
in En
| MEDLINE
| ID: mdl-35839222
ABSTRACT
Unplanned hospital readmissions mean a significant burden for health systems. Accurately estimating the patient's readmission risk could help to optimise the discharge decision-making process by smartly ordering patients based on a severity score, thus helping to improve the usage of clinical resources. A great number of heterogeneous factors can influence the readmission risk, which makes it highly difficult to be estimated by a human agent. However, this score could be achieved with the help of AI models, acting as aiding tools for decision support systems. In this paper, we propose a machine learning classification and risk stratification approach to assess the readmission problem and provide a decision support system based on estimated patient risk scores.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Patient Discharge
/
Patient Readmission
Type of study:
Etiology_studies
/
Health_economic_evaluation
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
PLoS One
Journal subject:
CIENCIA
/
MEDICINA
Year:
2022
Type:
Article
Affiliation country:
Spain