Temporal Abstractions for Diabetic Patients Management
AIME '97 Proceedings of the 6th Conference on Artificial Intelligence in Medicine in Europe
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
Querying temporal clinical databases on granular trends
Journal of Biomedical Informatics
AI in medicine on its way from knowledge-intensive to data-intensive systems
Artificial Intelligence in Medicine
Intelligent analysis of clinical time series: an application in the diabetes mellitus domain
Artificial Intelligence in Medicine
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
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On-line monitoring at neonatal intensive care units produces high volumes of data. Numerous devices generate data at high frequency (one data set every second). Both, the high volume and the quite high error-rate of the data make it essential to reach at higher levels of description from such raw data. These abstractions should improve the medical decision making. We will present a time-oriented data-abstraction method to derive steady qualitative descriptions from oscillating high-frequency data. The method contains tunable parameters to guide the sensibility of the abstraction process. The benefits and limitations of the different parameter settings will be discussed.