Development of a scoring tool for predicting prolonged length of hospital stay in peritoneal dialysis patients through data mining.

ABSTRACT:

Background

The hospital admission rate is high in patients treated with peritoneal dialysis (PD), and the length of stay (LOS) in the hospital is a key indicator of medical resource allocation. This study aimed to develop a scoring tool for predicting prolonged LOS (pLOS) in PD patients by combining machine learning and traditional logistic regression (LR).

Methods

This study was based on patient data collected using the Hospital Quality Monitoring System (HQMS) in China. Three machine learning methods, classification and regression tree (CART), random forest (RF), and gradient boosting decision tree (GBDT), were used to develop models to predict pLOS, which is longer than the average LOS, in PD patients. The model with the best prediction performance was used to identify pr

SUBMITTER: Wu J 

PROVIDER: S-EPMC7723539 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature