TY - GEN
T1 - Predicting Length of Hospital Stay Using Machine Learning
AU - Ito, Mari
AU - Takeuchi, Kanade
AU - Suzuki, Masaaki
AU - Aaron, Aurelius
AU - Koizumi, Masaki
AU - Yano, Akemi
AU - Inokuchi, Sadaki
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024/7
Y1 - 2024/7
N2 - Bed management directly impacts the amount of medical care provided to patients and the economic efficiency of hospitals. Beds in hospitals are managed according to the length of stay (LOS) in the hospital, as expected by doctors. The efficiency of bed management would improve if the LOS could be accurately predicted. This study focuses on predicting the LOS for multiple diseases in short-term inpatient management. The data in short-term inpatient records from Ebina General Hospital are used for predicting the LOS requirements using a machine learning (ML) algorithm. We demonstrate the relation-ship between the prediction accuracy and ML algorithm. This study provides insight into the prediction of the short-term LOS on a daily basis.
AB - Bed management directly impacts the amount of medical care provided to patients and the economic efficiency of hospitals. Beds in hospitals are managed according to the length of stay (LOS) in the hospital, as expected by doctors. The efficiency of bed management would improve if the LOS could be accurately predicted. This study focuses on predicting the LOS for multiple diseases in short-term inpatient management. The data in short-term inpatient records from Ebina General Hospital are used for predicting the LOS requirements using a machine learning (ML) algorithm. We demonstrate the relation-ship between the prediction accuracy and ML algorithm. This study provides insight into the prediction of the short-term LOS on a daily basis.
KW - clas-sification technique
KW - length of hospital stay
KW - machine learning
UR - https://www.scopus.com/pages/publications/85208138342
U2 - 10.1109/IIAI-AAI63651.2024.00141
DO - 10.1109/IIAI-AAI63651.2024.00141
M3 - Conference article published in proceeding or book
AN - SCOPUS:85208138342
T3 - Proceedings - 2024 16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024
SP - 695
EP - 697
BT - Proceedings - 2024 16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024
Y2 - 6 July 2024 through 12 July 2024
ER -