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.
|Title of host publication||Lecture Notes in Artificial Intelligence|
|Publication status||Published - 1 Jan 1999|
|Event||Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making - |
Duration: 1 Jan 1999 → …
|Conference||Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making|
|Period||1/01/99 → …|
Cruz, J. C. F. R. D., & Barahona, P. M. C. C. D. (1999). Integrating deep biomedical models into medical decision support systems: An interval constraint approach. In Lecture Notes in Artificial Intelligence (Vol. 1620, pp. 185-194) https://doi.org/10.1007/3-540-48720-4_20