Integrated medical resource consumption stratification in hospitalized patients: an Auto Triage Management model based on accurate risk, cost and length of stay prediction.
Sci China Life Sci
; 65(5): 988-999, 2022 05.
Article
em En
| MEDLINE
| ID: mdl-34632536
ABSTRACT
Triage management plays important roles in hospitalized patients for disease severity stratification and medical burden analysis. Although progression risks have been extensively researched for numbers of diseases, other crucial indicators that reflect patients' economic and time costs have not been systematically studied. To address the problems, we developed an automatic deep learning based Auto Triage Management (ATM) Framework capable of accurately modelling patients' disease progression risk and health economic evaluation. Based on them, we can first discover the relationship between disease progression and medical system cost, find potential features that can more precisely aid patient triage in resource allocation, and allow treatment plan searching that has cured patients. Applying ATM in COVID-19, we built a joint model to predict patients' risk, the total length of stay (LoS) and cost when at-admission, and remaining LoS and cost at a given hospitalized time point, with C-index 0.930 and 0.869 for risk prediction, mean absolute error (MAE) of 5.61 and 5.90 days for total LoS prediction in internal and external validation data.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Contexto em Saúde:
1_ASSA2030
Base de dados:
MEDLINE
Assunto principal:
Triagem
/
COVID-19
Tipo de estudo:
Etiology_studies
/
Health_economic_evaluation
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Sci China Life Sci
Ano de publicação:
2022
Tipo de documento:
Article