Learning Qualitative Models of Dynamic Systems
Machine Learning - special issue on inductive logic programming
Think!: a unified numerical-symbolic knowledge representation scheme and reasoning system
Artificial Intelligence
Deriving Trends in Historical and Real-Time Continuously Sampled Medical Data
Journal of Intelligent Information Systems - Special issue on integrating artificial intelligene and database technologies
The graphical presentation of decision support information in an intelligent anaesthesia monitor
Artificial Intelligence in Medicine
Temporal abstraction in intelligent clinical data analysis: A survey
Artificial Intelligence in Medicine
Real-time analysis of physiological data to support medical applications
IEEE Transactions on Information Technology in Biomedicine
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Data driven interpretation of multiple physiological measurements in the domain of intensive care is a key point to provide decision support. The abstraction method presented in this paper provides two levels of symbolic interpretation. The first, at mono parametric level, provides 4 classes (increasing, decreasing, constant and transient) by combination of trends computed at two characteristic spans. The second, at multi parametric level, gives an index of global behavior of the system, that is used to segment the observation. Each segment is therefore described as a sequence of words that combines the results of symbolization. Each step of the abstraction process leads to a visual representation that can be validated by the clinician. Construction of sequences do not need any prior introduction of medical knowledge. Sequences can be introduced in a machine learning process in order to extract temporal patterns related to specific clinical or technical events.