Predicting the length of hospital stay of burn patients: Comparisons of prediction accuracy among different clinical stages

Authors:
Chin-Sheng Yang;Chih-Ping Wei;Chi-Chuan Yuan;Jen-Yu Schoung
Affiliations:
Department of Information Management, College of Informatics, Yuan Ze University, Chungli, Taiwan, ROC;Department of Information Management, College of Management, National Taiwan University, Taipei, Taiwan, ROC;Zuoying Armed Forces General Hospital, Kaohsiung, Taiwan, ROC;Plastic & Reconstructive Department, Saint Mary's Hospital, Luodong, Ilan, Taiwan, ROC
Venue:
Decision Support Systems
Year:
2010

Citing 10
Cited 4

The nature of statistical learning theory

The nature of statistical learning theory
Wrappers for feature subset selection

Artificial Intelligence - Special issue on relevance
Induction of Decision Trees

Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A tutorial on support vector regression

Statistics and Computing
A hybrid model for exchange rate prediction

Decision Support Systems
Modeling wine preferences by data mining from physicochemical properties

Decision Support Systems
Travel-time prediction with support vector regression

IEEE Transactions on Intelligent Transportation Systems
A Model-Based Approach to the Analysis of Patterns of Length of Stay in Institutional Long-Term Care

IEEE Transactions on Information Technology in Biomedicine
Managing Clinical Use of High-Alert Drugs: A Supervised Learning Approach to Pharmacokinetic Data Analysis

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

Forecasting medical cost inflation rates: A model comparison approach

Decision Support Systems
Applying a BP neural network model to predict the length of hospital stay

HIS'13 Proceedings of the second international conference on Health Information Science
Length of stay prediction for clinical treatment process using temporal similarity

Expert Systems with Applications: An International Journal
Reprint of "Length of stay prediction for clinical treatment process using temporal similarity"

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

A burn injury is a disastrous trauma and can have wide-ranging impacts on burn patients, their families, and society. Burn patients generally experience long hospital stays, and the accurate prediction of the length of those stays has strong implications for healthcare resource management and service delivery. In addition to prediction accuracy, the timing of length of hospital stay (LOS) predictions is also relevant, because LOS predictions during earlier clinical stages (e.g., admission) can provide an important component for service and resource planning as well as patient and family counseling, whereas LOS predictions at later clinical stages (e.g., post-treatment) can support resource utilization reviews and cost controls. This study evaluates the effectiveness of LOS predictions for burn patients during three different clinical stages: admission, acute, and post-treatment. In addition, we compare the prediction effectiveness of two artificial intelligence (AI)-based prediction techniques (i.e., model-tree-based regression and support vector machine regression), using linear regression analysis as our performance benchmark. On the basis of 1080 burn cases collected in Taiwan, the empirical evaluation suggests that the accuracy of LOS predictions at the acute stage does not improve compared with those during the admission stage, but LOS predictions at the post-treatment stage are significantly more accurate. Moreover, the AI-based prediction techniques, especially support vector machine regression, appear more effective than the regression technique for LOS predictions for burn patients across stages.