Temporal reasoning and query answering with preferences and probabilities for medical decision support

Abstract

Knowledge-based decision support systems have a long tradition within the medical area. In particular, in the last decades, many Computer-Interpretable Guidelines (CIG) systems have been built to provide evidence-based and knowledge-based support to physicians. Since CIGs are, by definition, devoted to the management of specific diseases, the treatment of comorbid patients constitutes a challenging task in the area, involving (i) the detection of the possible interactions between (the effects of) the actions recommended by multiple CIGs (one for each disease of the patient), (ii) the management of such interactions and, finally, (iii) the conciliation of (the recommendations of) different CIGs. This paper focuses on issue (i) above, and specifically, on an innovative approach to support interaction detection along the temporal dimension. Practically, interactions can only occur between effects that intersect in time. Therefore, interaction detection involves the representation of temporal information (temporal constraints), and temporal reasoning (to propagate such constraints). Additionally, query answering facilities are important to support physicians in the investigation of the results of temporal reasoning. Current CIG approaches that face such issues take into account only “crisp” temporal constraints, i.e., they consider all temporal constraints as equally probable\preferred. However, preferences about the temporal constraints between CIGs actions may be available, as well as knowledge about the probabilistic distribution of the effects of CIGs actions in time. Considering such additional pieces of information can provide crucial advantages, in term of the flexibility and informativeness of the support provided by the CIG system to physicians. In this paper, we propose the first homogeneous approach to represent and reason with (propagate) temporal constraints with both preferences and probabilities. We ground our approach on the widely-used Simple Temporal Problem (STP) framework, which supports temporal reasoning on temporal constraints about possible distances between events. We extend (i) the representation formalism to associate preferences and\or probabilities to the possible distances, and (ii) the operations to propagate the constraints to combine also preferences and probabilities. We also (iii) provide an experimental evaluation of our approach, and (iv) propose a wide range of query-answering supports, to facilitate physicians in the analysis of the results of temporal reasoning in general, and in the temporal detection of possible interactions in particular. Finally, (v) we also show how such a temporal framework is integrated in GLARE-SSCPM, a CIG system to treat comorbid patients, and show the advantages of our approach considering a running example.

Publication
In Expert Systems with Applications. Volume 195, 1 June 2022, 116565