Distance based approaches to relational learning and clustering
Relational Data Mining
EPIA '99 Proceedings of the 9th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Machine Learning for Data Mining in Medicine
AIMDM '99 Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making
Handbook of data mining and knowledge discovery
Journal of the American Society for Information Science and Technology
Rough sets data analysis in knowledge discovery: a case of Kuwaiti diabetic children patients
Advances in Fuzzy Systems - Regular issue
INTCare: on-line knowledge discovery in the intensive care unit
INES'09 Proceedings of the IEEE 13th international conference on Intelligent Engineering Systems
A new weighted rough set framework based classification for Egyptian NeoNatal Jaundice
Applied Soft Computing
Predicting glaucomatous visual field deterioration through short multivariate time series modelling
Artificial Intelligence in Medicine
Hi-index | 0.00 |
From the Publisher:Intelligent Data Analysis in Medicine and Pharmacology consists of selected (and thoroughly revised) papers presented at the First International Workshop on Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-96) held in Budapest in August 1996 as part of the 12th European Conference on Artificial Intelligence (ECAI-96). IDAMAP-96 was organized with the motivation to gather scientists and practitioners interested in computational data analysis methods applied to medicine and pharmacology, aimed at narrowing the increasing gap between excessive amounts of data stored in medical and pharmacological databases on the one hand, and the interpretation, understanding and effective use of stored data on the other hand. Besides the revised Workshop papers, the book contains a selection of contributions by invited authors. The expected readership of Intelligent Data Analysis in Medicine and Pharmacology is researchers and practitioners interested in intelligent data analysis, data mining, and knowledge discovery in databases, particularly those who are interested in using these technologies in medicine and pharmacology. Researchers and students in artificial intelligence and statistics should find this book of interest as well. Finally, much of the presented material will be interesting to physicians and pharmacologists challenged by new computational technologies, or simply in need of effectively utilizing the overwhelming volumes of data collected as a result of improved computer support in their daily professional practice.