A theory of diagnosis from first principles
Artificial Intelligence
Artificial Intelligence - Special issue on knowledge representation
Characterizing diagnoses and systems
Artificial Intelligence
Using Time-Oriented Data Abstraction Methods to Optimize Oxygen Supply for Neonates
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
Temporal abstraction in intelligent clinical data analysis: A survey
Artificial Intelligence in Medicine
Knowledge construction from time series data using a collaborative exploration system
Journal of Biomedical Informatics
An approach to self-adaptive software based on supervisory control
IWSAS'01 Proceedings of the 2nd international conference on Self-adaptive software: applications
Learning rules from multisource data for cardiac monitoring
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
Model based diagnosis and contexts in self adaptive software
Self-star Properties in Complex Information Systems
Artificial Intelligence in Medicine
A method for automated temporal knowledge acquisition applied to sleep-related breathing disorders
Artificial Intelligence in Medicine
Logic-based representation, reasoning and machine learning for event recognition
Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems
Event processing under uncertainty
Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems
| Hi-index | 0.00 |
Monitoring patients in intensive care units is a critical task. Simple condition detection is generally insufficient to diagnose a patient and may generate many false alarms to the clinician operator. Deeper knowledge is needed to discriminate among alarms those that necessitate urgent therapeutic action. We propose an intelligent monitoring system that makes use of many artificial intelligence techniques: artificial neural networks for temporal abstraction, temporal reasoning, model based diagnosis, decision rule based system for adaptivity and machine learning for knowledge acquisition. To tackle the difficulty of taking context change into account, we introduce a pilot aiming at adapting the system behavior by reconfiguring or tuning the parameters of the system modules. A prototype has been implemented and is currently experimented and evaluated. Some results, showing the benefits of the approach, are given.