The events which occur in an Intensive Care Unit (ICU) are many and varied. Very often, events which are important to an understanding of what has happened to the patient are not recorded in the electronic patient record. This paper describes an approach to the automatic detection of such unrecorded 'target' events which brings together signal analysis to generate temporal patterns, and temporal constraint networks to integrate these patterns with other associated events which are manually or automatically recorded. This approach has been tested on real data recorded in a Neonatal ICU with positive results.
|Title of host publication||AIME-09|
|Subtitle of host publication||Proceedings of the Twelfth European Conference on Artificial Intelligence in Medicine|
|Editors||Carlo Combi, Yuval Shahar, Ameen Abu-Hanna|
|Number of pages||10|
|Publication status||Published - 10 Jul 2009|
|Name||Lecture Notes in Artificial Intelligence|