Your browser doesn't support javascript.
Integrated medical resource consumption stratification in hospitalized patients: an Auto Triage Management model based on accurate risk, cost and length of stay prediction.
Zhong, Qin; Li, Zongren; Wang, Wenjun; Zhang, Lei; He, Kunlun.
  • Zhong Q; Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, 100853, China.
  • Li Z; Medical Artificial Intelligence Research Center, Chinese PLA General Hospital, Beijing, 100853, China.
  • Wang W; Bio-engineering Research Center, Chinese PLA General Hospital, Beijing, 100039, China.
  • Zhang L; China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, 710061, China.
  • He K; Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, 100853, China. kunlunhe301@gmail.com.
Sci China Life Sci ; 65(5): 988-999, 2022 05.
Article in English | MEDLINE | ID: covidwho-1460457
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.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: Triage / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Limits: Humans Language: English Journal: Sci China Life Sci Journal subject: Biology / Science Year: 2022 Document Type: Article Affiliation country: S11427-021-1987-5

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Triage / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Limits: Humans Language: English Journal: Sci China Life Sci Journal subject: Biology / Science Year: 2022 Document Type: Article Affiliation country: S11427-021-1987-5