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RATIONALE
Artificial Intelligence in Medicine is facing a new challenge, created by the rapid growth in information science and technology in general and the complexity and volume of data in particular. Medical settings are using sensors and networks of health information systems to integrate data from patients, which requires storage, processing and management operators to enable further analysis and knowledge discovery. The main issue is that this data production often takes the form of high-speed continuous flows of data, i.e. data streams. Medical domains include several settings where data is produced in a streaming fashion, such as anatomical and physiological sensors, or incidence records and health information systems. New services like Google Health appear allowing users to store and track information about their medical history, to connect to and stream data from medical devices. Medical data streams become widespread and call for development of intelligent tools for making use of these data. Decision support, alerting services, ambient intelligence, assisted leaving and personalization services are just few examples of expected uses of actionable knowledge extracted from medical data streams. All of them are characterized by the high-speed at which huge amounts of data are produced, and often require fast and accurate information retrieval and analysis, that can effectively support clinical decisions. Dealing with continuous, and possibly infinite, flows of data require different approaches for artificial intelligence in medicine. Particular issues to address include summarization of infinite data, resource-awareness, real-time monitoring of changes and recurrences, incremental and decremental learning, etc. This is an incremental task that requires incremental algorithms that integrate artificial intelligence in medical domains. Streaming artificial intelligence is increasingly important in the research community, as new algorithms are needed to process medical data in reasonable time. Furthermore, medical domains introduce extra peculiarities to the problem. For example, health information systems now deal with heterogeneous data sources, possibly distributed across healthcare institutions. Moreover, this data integration requirement yields possibly privacy-preserving issues, the same time it forces the system to take time, resources, and costs into consideration. Currently, generic techniques for intelligent analysis and artificial intelligence for streaming data are widely spread in the artificial intelligence research community. Also, in the medical domain technological issues of data collection and storage, access, integration, information fusion, etc are also widely studied in the health informatics research community. However, adoption and development of tailored techniques for medical data stream mining and clinical decision support is still to come. The goal of this workshop is to bring together experts in data stream artificial intelligence interested in medical applications and medical domain experts interested in timely analysis of their data streams for clinical decision support. |