Artificial Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Decision Support Systems - Special issue: Decision support systems: Directions for the next decade
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
The art of artificial intelligence: I. Themes and case studies of knowledge engineering
The art of artificial intelligence: I. Themes and case studies of knowledge engineering
Predicting good probabilities with supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Portal Information Integration and Ownership Misfits: A Case Study in a Tourism Setting
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 08
A note on Platt's probabilistic outputs for support vector machines
Machine Learning
IEEE Transactions on Information Technology in Biomedicine
A decision support system for cost-effective diagnosis
Artificial Intelligence in Medicine
Rough sets based association rules application for knowledge-based system design
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
An analytic approach to better understanding and management of coronary surgeries
Decision Support Systems
Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis System
Journal of Medical Systems
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
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This study presents a new method for constructing an expert system using a hospital referral problem as an example. Many factors, such as institutional characteristics, patient risks, traveling distance, and chances of survival and complications should be included in the hospital-selection decision. Ideally, each patient should be treated individually, with the decision process including not only their condition but also their beliefs about trade-offs among the desired hospital features. An expert system can help with this complex decision, especially when numerous factors are to be considered. We propose a new method, called the Prediction and Optimization-Based Decision Support System (PODSS) algorithm, which constructs an expert system without an explicit knowledge base. The algorithm obtains knowledge on its own by building machine learning classifiers from a collection of labeled cases. In response to a query, the algorithm gives a customized recommendation, using an optimization step to help the patient maximize the probability of achieving a desired outcome. In this case, the recommended hospital is the optimal solution that maximizes the probability of the desired outcome. With proper formulation, this expert system can combine multiple factors to give hospital-selection decision support at the individual level.