Length-of-Stay Prediction for Pediatric Patients with Respiratory Diseases Using Decision Tree Methods

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

6 Scopus Citations
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Detail(s)

Original languageEnglish
Article number9007437
Pages (from-to)2651-2662
Journal / PublicationIEEE Journal of Biomedical and Health Informatics
Volume24
Issue number9
Online published24 Feb 2020
Publication statusPublished - Sep 2020
Externally publishedYes

Abstract

Accurate prediction of a patient's length-of-stay (LOS) in the hospital enables an efficient and effective management of hospital beds. This paper studies LOS prediction for pediatric patients with respiratory diseases using three decision tree methods: Bagging, Adaboost, and Random forest. A data set of 11,206 records retrieved from the hospital information system is used for analysis after preprocessing and transformation through a computation and an expansion method. Two tests, namely bisection test and periodic test, are designed to assess the performance of the prediction methods. Bagging shows the best result on the bisection test (0.296 RMSE, 0.831 R2, and 0.723 Acc ± 1) for the testing set of the whole data test. The performances of the three methods are similar on the periodic test, whereas Adaboost performs slightly better than the other two methods. Results indicate that the three methods are all effective for the LOS prediction. This study also investigates the importance of different data fields to the LOS prediction, and finds that hospital treatment-related data fields contribute more to the LOS prediction than other categories of fields.

Research Area(s)

  • decision tree, length-of-stay prediction, Machine learning

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