Integrating deep biomedical models into medical decision support systems: An interval constraint approach

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

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.
Original languageUnknown
Title of host publicationLecture Notes in Artificial Intelligence
Pages185-194
Volume1620
DOIs
Publication statusPublished - 1 Jan 1999
EventJoint European Conference on Artificial Intelligence in Medicine and Medical Decision Making -
Duration: 1 Jan 1999 → …

Conference

ConferenceJoint European Conference on Artificial Intelligence in Medicine and Medical Decision Making
Period1/01/99 → …

Cite this

@inproceedings{07d98d44421c4470804e5338149477f8,
title = "Integrating deep biomedical models into medical decision support systems: An interval constraint approach",
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.",
author = "Cruz, {Jorge Carlos Ferreira Rodrigues da} and Barahona, {Pedro Manuel Corr{\^e}a Calvente de}",
year = "1999",
month = "1",
day = "1",
doi = "10.1007/3-540-48720-4_20",
language = "Unknown",
isbn = "3-540-66162-X",
volume = "1620",
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booktitle = "Lecture Notes in Artificial Intelligence",

}

Cruz, JCFRD & Barahona, PMCCD 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, Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making, 1/01/99. https://doi.org/10.1007/3-540-48720-4_20

Integrating deep biomedical models into medical decision support systems: An interval constraint approach. / Cruz, Jorge Carlos Ferreira Rodrigues da; Barahona, Pedro Manuel Corrêa Calvente de.

Lecture Notes in Artificial Intelligence. Vol. 1620 1999. p. 185-194.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - 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.

AB - 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.

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