C4.5: programs for machine learning
C4.5: programs for machine learning
Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
Inductive logic programming for knowedge discovery in databases
Relational Data Mining
Knowledge discovery from data?
IEEE Intelligent Systems
Machine Learning
Medical Knowledge Discovery on the Meningoencephalitis Diagnosis Studied by the Cascade Model
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Exploratory medical knowledge discovery: experiences and issues
ACM SIGKDD Explorations Newsletter
Subgroup Discovery with CN2-SD
The Journal of Machine Learning Research
Induction of comprehensible models for gene expression datasets by subgroup discovery methodology
Journal of Biomedical Informatics - Special issue: Biomedical machine learning
Expert-guided subgroup discovery: methodology and application
Journal of Artificial Intelligence Research
RSD: relational subgroup discovery through first-order feature construction
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
From local to global patterns: evaluation issues in rule learning algorithms
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Active subgroup mining: a case study in coronary heart disease risk group detection
Artificial Intelligence in Medicine
Editorial: Bayesian networks in biomedicine and health-care
Artificial Intelligence in Medicine
Supporting Factors in Descriptive Analysis of Brain Ischaemia
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
A machine learning and data mining framework to enable evolutionary improvement in trauma triage
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
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This paper presents a case study of the process of insightful analysis of clinical data collected in regular hospital practice. The approach is applied to a database describing patients suffering from brain ischaemia, either permanent as brain stroke with positive computer tomography (CT) or reversible ischaemia with normal brain CT test. The goal of the analysis is the extraction of useful knowledge that can help in diagnosis, prevention and better understanding of the vascular brain disease. This paper demonstrates the applicability of subgroup discovery for insightful data analysis and describes the expert's process of converting the induced rules into useful medical knowledge. Detection of coexisting risk factors, selection of relevant discriminative points for numerical descriptors, as well as the detection and description of characteristic patient subpopulations are important results of the analysis. Graphical representation is extensively used to illustrate the detected dependencies in the available clinical data.