Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A framework for knowledge-based temporal abstraction
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
Scalable Parallel Computing: Technology,Architecture,Programming
Scalable Parallel Computing: Technology,Architecture,Programming
Automated trend detection with alternate temporal hypotheses
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
A framework for distributed mediation of temporal-abstraction queries to clinical databases
Artificial Intelligence in Medicine
Temporal abstraction in intelligent clinical data analysis: A survey
Artificial Intelligence in Medicine
Evaluation of an architecture for intelligent query and exploration of time-oriented clinical data
Artificial Intelligence in Medicine
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Several systems have been designed to reason about longitudinal patient data in terms of abstract, clinically meaningful concepts derived from raw time-stamped clinical data. However, current approaches are limited by their treatment of missing data and of the inherent uncertainty that typically underlie clinical raw data. Furthermore, most approaches have generally focused on a single patient. We have designed a new probability-oriented methodology to overcome these conceptual and computational limitations. The new method includes also a practical parallel computational model that is geared specifically for implementing our probabilistic approach in the case of abstraction of a large number of electronic medical records.