Integrating Deep Biomedical Models into Medical Decision Support Systems: An Interval Constraint Approach

Authors:
Jorge Cruz;Pedro Barahona;Frédéric Benhamou
Affiliations:
-;-;-
Venue:
AIMDM '99 Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making
Year:
1999

Citing 5
Cited 0

CLP(intervals) revisited

ILPS '94 Proceedings of the 1994 International Symposium on Logic programming
Ordinary Differential Equations

Ordinary Differential Equations
Consistency Techniques in Ordinary Differential Equations

CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
A Causal-Functional Model Applied to EMG Diagnosis

AIME '97 Proceedings of the 6th Conference on Artificial Intelligence in Medicine in Europe
A Method for Diagnosing in Large Medical Expert Systems Based on Causal Probabilistic Networks

AIME '97 Proceedings of the 6th Conference on Artificial Intelligence in Medicine in Europe

Quantified Score

Hi-index	0.00

Visualization

Abstract

Knowledge representation has always been a major problem in the design of medical decision support systems. In this paper we present a new methodology to represent and reason about medical knowledge, based on the declarative specification of interval constraints over the medical concepts. This allows the integration of deep medical models involving differential equations developed in biomedical research (typical in several medical domains) which, due to their complexity, have not been incorporated into medical decision support systems. The methodology which enables reasoning both forward and backward in time, is applied to a specific domain, electromyography. The promising results obtained are discussed to justify our future work.