The nature of statistical learning theory
The nature of statistical learning theory
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Information Retrieval
Statistical Learning Theory and State of the Art in SVM
ICCI '03 Proceedings of the 2nd IEEE International Conference on Cognitive Informatics
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Class imbalances versus small disjuncts
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A tutorial on support vector regression
Statistics and Computing
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Learning from imbalanced data in surveillance of nosocomial infection
Artificial Intelligence in Medicine
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Application of digital ecosystem design methodology within the health domain
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Hi-index | 0.00 |
Skyrocketing patient-care costs demand that health-care institutions improve their resource-utilization effectiveness and efficiency. The length of an inpatient's stay has direct significant impacts on patient-care costs, service quality, and outcomes. Despite attempts to manage the length of stay (LOS) for frequently performed surgical procedures (e.g., appendectomies), many service providers cannot achieve the target range allowed by the managed care system. We take a data-driven approach to predict which appendectomy patients will likely have a LOS beyond that reimbursable by the underlying managed care system. We use a support vector machine to construct a generic prediction system and then extend that system by incorporating a resampling or cost-sensitive method to address the imbalanced sample problem. Using 557 appendectomy cases from a tertiary medical center in Taiwan, we examine the effectiveness of the generic prediction system compared with the effectiveness of its extensions. The results suggest the viability of a data-driven approach to manage LOS by enabling service providers to identify in advance those patients who will likely need extended stays. The comparative analyses also show the relative advantages of the oversampling and cost-sensitive methods for addressing the imbalanced sample problem. The findings have important implications for research and practice.