Conformance analysis for comorbid patients in Answer Set Programming.

Autor: Piovesan L; DISIT, Institute of Computer Science, Università del Piemonte Orientale, Alessandria, Italy. Electronic address: luca.piovesan@uniupo.it., Terenziani P; DISIT, Institute of Computer Science, Università del Piemonte Orientale, Alessandria, Italy. Electronic address: paolo.terenziani@uniupo.it., Theseider Dupré D; DISIT, Institute of Computer Science, Università del Piemonte Orientale, Alessandria, Italy. Electronic address: dtd@uniupo.it.
Jazyk: angličtina
Zdroj: Journal of biomedical informatics [J Biomed Inform] 2020 Mar; Vol. 103, pp. 103377. Date of Electronic Publication: 2020 Jan 27.
DOI: 10.1016/j.jbi.2020.103377
Abstrakt: The treatment of comorbid patients is a hot problem in Medical Informatics, since the plain application of multiple Computer-Interpretable Guidelines (CIGs) can lead to interactions that are potentially dangerous for the patients. The specialized literature has mostly focused on the "a priori" or "execution-time" analysis of the interactions between multiple Computer-Interpretable Guidelines (CIGs), and/or CIG "merge". In this paper, we face a complementary problem, namely, the a posteriori analysis of the treatment of comorbid patients. Given the CIGs, the history of the status of the patient, and the log of the clinical actions executed on her, we try to explain the actions executed on the patient (i.e., the log) in terms of the actions recommended by the CIGs, of their potential interactions, and of the possible ways of managing each such interaction, pointing out (i) deviations from CIG recommendations not explained in terms of interaction management (if any) and (ii) unmanaged interactions (if any). Our approach is based on Answer Set Programming, and, to face realistic problems, devotes specific attention to the temporal dimension.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2020 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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