C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Ontology-based extraction and structuring of information from data-rich unstructured documents
Proceedings of the seventh international conference on Information and knowledge management
Decision support systems in the twenty-first century
Decision support systems in the twenty-first century
Accessing heterogenous sources of evidence to answer clinical questions
Computers and Biomedical Research
MAFRA - A MApping FRAmework for Distributed Ontologies
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Ontobroker: Ontology Based Access to Distributed and Semi-Structured Information
DS-8 Proceedings of the IFIP TC2/WG2.6 Eighth Working Conference on Database Semantics- Semantic Issues in Multimedia Systems
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Ontology mapping: the state of the art
The Knowledge Engineering Review
IEEE Transactions on Knowledge and Data Engineering
Multi-labelled classification using maximum entropy method
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic Fuzzy Ontology Generation for Semantic Web
IEEE Transactions on Knowledge and Data Engineering
ACM SIGMOD Record
Medical Knowledge Morphing via a Semantic Web Framework
CBMS '07 Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems
N3logic: A logical framework for the world wide web
Theory and Practice of Logic Programming
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Knowledge and reasoning for medical question-answering
KRAQ '09 Proceedings of the 2009 Workshop on Knowledge and Reasoning for Answering Questions
Sharing e-Health Information through Ontological Layering
HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
GP classification under imbalanced data sets: active sub-sampling and AUC approximation
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Information Retrieval: Implementing and Evaluating Search Engines
Information Retrieval: Implementing and Evaluating Search Engines
Clinical Decision Support Systems: Theory and Practice
Clinical Decision Support Systems: Theory and Practice
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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In this article, we begin by presenting OMeD, a medical decision support system, and argue for its value over purely probabilistic approaches that reason about patients for time-critical decision scenarios. We then progress to present Holmes, a Hybrid Ontological and Learning MEdical System which supports decision making about patient treatment. This system is introduced in order to cope with the case of missing data. We demonstrate its effectiveness by operating on an extensive set of real-world patient health data from the CDC, applied to the decision-making scenario of administering sleeping pills. In particular, we clarify how the combination of semantic, ontological representations, and probabilistic reasoning together enable the proposal of effective patient treatments. Our focus is thus on presenting an approach for interpreting medical data in the context of real-time decision making. This constitutes a comprehensive framework for the design of medical recommendation systems for potential use by medical professionals and patients both, with the end result being personalized patient treatment. We conclude with a discussion of the value of our particular approach for such diverse considerations as coping with misinformation provided by patients, performing effectively in time-critical environments where real-time decisions are necessary, and potential applications facilitating patient information gathering.