A framework for knowledge-based temporal abstraction
Artificial Intelligence
Intelligent Data Analysis: An Introduction
Intelligent Data Analysis: An Introduction
Abstracting Steady Qualitative Descriptions over Time from Noisy, High-Frequency Data
AIMDM '99 Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making
Knowledge-Based Event Detection in Complex Time Series Data
AIMDM '99 Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making
AIMDM '99 Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making
Temporal Abstractions for Diabetic Patients Management
AIME '97 Proceedings of the 6th Conference on Artificial Intelligence in Medicine in Europe
Temporal abstraction in intelligent clinical data analysis: A survey
Artificial Intelligence in Medicine
Temporal Abstraction of States Through Fuzzy Temporal Constraint Networks
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
Intelligent adaptive monitoring for cardiac surveillance
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Using temporal constraints for temporal abstraction
Journal of Intelligent Information Systems
AI in medicine on its way from knowledge-intensive to data-intensive systems
Artificial Intelligence in Medicine
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Therapy management needs sophisticated patient monitoring and therapy planning, especially in high-frequency domains, like Neonatal Intensive Care Units (NICUs), where complex data sets are collected every second. An elegant method to tackle this problem is the use of time-oriented, skeletal plans. Asgaard is a framework for the representation, visualization, and execution of such plans. These plans work on qualitative abstracted time-oriented data which closely resemble the concepts used by experienced clinicians.This papers presents the data abstraction unit of the Asgaard system. It provides a range of connectable data abstraction methods bridging the gap between the raw data collected by monitoring devices and the abstract concepts used in therapeutic plans. The usability of this data abstraction unit is demonstrated by the implementation of a controller for the automated optimization of the fraction of inspired oxygen (FiO2). The use of the time-oriented data abstraction methods results in safe and smooth adjustment actions of our controller in a neonatal care setting.