C4.5: programs for machine learning
C4.5: programs for machine learning
Decision Trees: An Overview and Their Use in Medicine
Journal of Medical Systems
Sequence Learning: From Recognition and Prediction to Sequential Decision Making
IEEE Intelligent Systems
Machine Learning
A Machine Learning Approach to Workflow Management
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Workflow mining: a survey of issues and approaches
Data & Knowledge Engineering
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Building Bayesian Network Models in Medicine: The MENTOR Experience
Applied Intelligence
Test Strategies for Cost-Sensitive Decision Trees
IEEE Transactions on Knowledge and Data Engineering
Increasing Acceptability of Decision Trees with Domain Attributes Partial Orders
CBMS '07 Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems
A Causal Modeling Framework for Generating Clinical Practice Guidelines from Data
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
On comparing two sequences of numbers and its applications to clustering analysis
Information Sciences: an International Journal
Improving medical decision trees by combining relevant health-care criteria
Expert Systems with Applications: An International Journal
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In medicine, decision processes are correct not only if they conclude with a right final decision, but also if the sequence of observations that drive the whole process to the final decision defines a sequence with a medical sense. Decision trees are formal structures that have been successfully applied to make decisions in medicine; however, the traditional machine learning algorithms used to induce these trees use information gain or cost ratios that cannot guarantee that the sequences of observations described by the induced trees have a medical sense. Here, we propose a slight variation of classical decision tree structures, provide four quality ratios to measure the medical correctness of a decision tree, and introduce a machine learning algorithm to induce medical decision trees whose final decisions are both correct and the result of a sequence of observations with a medical sense. The algorithm has been tested with four medical decision problems, and the successful results discussed.