Learning from medical data streams: an introduction

P. Pereira Rodrigues, M. Pechenizkiy, M.M. Gaber, J. Gama

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademic

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Abstract

Clinical practice and research are facing a new challenge created by the rapid growth of health information science and technology, and the complexity and volume of biomedical data. Machine learning from medical data streams is a recent area of research that aims to provide better knowledge extraction and evidence-based clinical decision support in scenarios where data are produced as a continuous flow. This year's edition of AIME, the Conference on Artificial Intelligence in Medicine, enabled the sound discussion of this area of research, mainly by the inclusion of a dedicated workshop. This paper is an introduction to LEMEDS, the Learning from Medical Data Streams workshop, which highlights the contributed papers, the invited talk and expert panel discussion, as well as related papers accepted to the main conference.
Original languageEnglish
Title of host publicationProceedings of the Workshop on Learning from Medical Data Streams (Bled, Slovania, July 6, 2011)
Place of PublicationAachen
PublisherCEUR-WS.org
Pages1-6
Publication statusPublished - 2011
Eventconference; Workshop on Learning from Medical Data Streams -
Duration: 1 Jan 2011 → …

Publication series

NameCEUR Workshop Proceedings
Volume765
ISSN (Print)1613-0073

Conference

Conferenceconference; Workshop on Learning from Medical Data Streams
Period1/01/11 → …
OtherWorkshop on Learning from Medical Data Streams

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