ResearchBib Share Your Research, Maximize Your Social Impacts
Sign for Notice Everyday Sign up >> Login

LEMEDS 2011 - Learning from Medical Data Streams LEMEDS'11



VenueBled, Slovenia Slovenia



Topics/Call fo Papers

Learning from Medical Data Streams
13th Conference on Artificial Intelligence in Medicine

Bled, Slovenia - July 6th, 2011



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 from which it is necessary to extract some sort of knowledge. The main issue is that this data production often takes the form of high-speed continuous flows of data.

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 tool 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 machine learning and knowledge discovery. Particular issues to address include summarization of infinite data, incremental and decremental learning, resource-awareness, real-time monitoring of changes and recurrences, etc. This is an incremental task that requires incremental learning 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 learning 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 learning from streaming data are widely spread in the machine learning 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 stream mining and clinical decision support is still to come.

The goal of this workshop is to bring together experts in data stream mining interested in medical applications and medical domain experts interested in timely analysis of their data streams for clinical decision support.


The topics include but are not restricted to:
Learning from anatomical sensor data streams
Learning from physiological sensor data streams
Knowledge discovery and decision support from biomedical signals
Knowledge discovery and decision support from electronic health records
Knowledge discovery and decision support from incidence records
Integrated health information data streams
Adaptive health information systems
Medical data stream models
Learning from ubiquitous medical data streams
Data streams integration in intensive care units
Remote monitoring of patients in hospital settings
Remote monitoring of patients in ambulatory settings
Process mining from medical data streams
Case reports of medical scenarios where data is produced in a stream
Real-time and real-world applications using streaming medical data


Workshop: 1 May 2011
15 May 2011
8 Jun 2011
6 Jul 2011


We invite submissions of either demo papers, short position papers or full research papers.

Papers should be written in English, according to Springer LNCS format (available here) and should not exceed 12 pages including figures, tables and references.

Selected papers will be peer-reviewed by at least two reviewers for elegibility and quality.

Accepted papers will appear in workshop proceedings that will be distributed among participants. Workshop proceedings will also be available electronically at the workshop web-page.

Successful authors with high-quality accepted papers will be invited to publish extended versions of their papers as chapters in a book edited by the workshop chairs.

At least one co-author of an accepted workshop paper should be present at AIME, and has to register also to the main conference (in addition to registration to the workshop).

Last modified: 2011-04-30 12:14:20