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Data and Knowledge for Medical Decision Support
Ensuring patient safety and providing high-quality health services are the dominant challenges faced by healthcare systems around the world today. The sharing of advanced knowledge and best practice in diagnosis, therapy, process optimization and prevention are essential to achieve this goal; this includes enhanced networking socially and technologically as well as the inclusion of public health and social sciences.This book contains the proceedings of the 13th European Federation for Medical Informatics (EFMI) Special Topic Conference (STC), held in Prague, Czech Republic, in April 2013. The EFMI STC 2013 is Europe's leading forum for presenting the results of current scientific work in hea...
Knowledge Discovery in Databases: PKDD 2003
This book constitutes the refereed proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2003, held in Cavtat-Dubrovnik, Croatia in September 2003 in conjunction with ECML 2003. The 40 revised full papers presented together with 4 invited contributions were carefully reviewed and, together with another 40 ones for ECML 2003, selected from a total of 332 submissions. The papers address all current issues in data mining and knowledge discovery in databases including data mining tools, association rule mining, classification, clustering, pattern mining, multi-relational classifiers, boosting, kernel methods, learning Bayesian networks, inductive logic programming, user preferences mining, time series analysis, multi-view learning, support vector machine, pattern mining, relational learning, categorization, information extraction, decision making, prediction, and decision trees.
Data Mining and Decision Support
Data mining deals with finding patterns in data that are by user-definition, interesting and valid. It is an interdisciplinary area involving databases, machine learning, pattern recognition, statistics, visualization and others. Decision support focuses on developing systems to help decision-makers solve problems. Decision support provides a selection of data analysis, simulation, visualization and modeling techniques, and software tools such as decision support systems, group decision support and mediation systems, expert systems, databases and data warehouses. Independently, data mining and decision support are well-developed research areas, but until now there has been no systematic attempt to integrate them. Data Mining and Decision Support: Integration and Collaboration, written by leading researchers in the field, presents a conceptual framework, plus the methods and tools for integrating the two disciplines and for applying this technology to business problems in a collaborative setting.
Informatics for Health: Connected Citizen-Led Wellness and Population Health
Over recent years there has been major investment in research infrastructure to harness the potential of routinely collected health data. In 2013, The Farr Institute for Health Informatics Research was established in the UK, undertaking health informatics research to enhance patient and public health by the analysis of data from multiple sources and unleashing the value of vast sources of clinical, biological, population and environmental data for public benefit. The Medical Informatics Europe (MIE) conference is already established as a key event in the calendar of the European Federation of Medical Informatics (EFMI); The Farr Institute has been establishing a conference series. For 2017, ...
Principles of Data Mining and Knowledge Discovery
This book constitutes the refereed proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery, PKDD 2002, held in Helsinki, Finland in August 2002. The 39 revised full papers presented together with 4 invited contributions were carefully reviewed and selected from numerous submissions. Among the topics covered are kernel methods, probabilistic methods, association rule mining, rough sets, sampling algorithms, pattern discovery, web text mining, meta data clustering, rule induction, information extraction, dependency detection, rare class prediction, classifier systems, text classification, temporal sequence analysis, unsupervised learning, time series analysis, medical data mining, etc.
ECAI 2004
This is the Golden Age for Artificial Intelligence. The world is becoming increasingly automated and wired together. This also increases the opportunities for AI to help people and commerce. Almost every sub field of AI had now been used in substantial applications. Some of the fields highlighted in this publication are: CBR Technology; Model Based Systems; Data Mining and Natural Language Techniques. Not only does this publication show the activities, capabilities and accomplishments of the sub fields, it also focuses on what is happening across the field as a whole.
Machine Learning
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