Generating conflict-free treatments for patients with comorbidity using ASP

Conflicts in recommended medical interventions regularly arise when multiple treatments are simultaneously needed for patients with comorbid diseases. An approach that can automatically repair such inconsistencies and generate conflict-free combined treatments is thus a valuable aid for clinicians. In this paper we propose an answer set programming based method that detects and repairs conflicts between treatments. The answer sets of the program directly correspond to proposed treatments, accounting for multiple possible solutions if they exist. We also include the possibility to take preferences based on drug-drug interactions into account while solving inconsistencies. We show in a case study that our method results in more preferred treatments

Temporal detection and analysis of guideline interactions

Background Clinical practice guidelines (CPGs) are assuming a major role in the medical area, to grant the quality of medical assistance, supporting physicians with evidence-based information of interventions in the treatment of single pathologies. The treatment of patients affected by multiple diseases (comorbid patients) is one of the main challenges for the modern healthcare. It requires the development of new methodologies, supporting physicians in the treatment of interactions between CPGs. Several approaches have started to face such a challenging problem. However, they suffer from a substantial limitation: they do not take into account the temporal dimension. Indeed, practically speaking, interactions occur in time. For instance, the effects of two actions taken from different guidelines may potentially conflict, but practical conflicts happen only if the times of execution of such actions are such that their effects overlap in time. Objectives We aim at devising a methodology to detect and analyse interactions between CPGs that considers the temporal dimension. Methods In this paper, we first extend our previous ontological model to deal with the fact that actions, goals, effects and interactions occur in time, and to model both qualitative and quantitative temporal constraints between them. Then, we identify different application scenarios, and, for each of them, we propose different types of facilities for user physicians, useful to support the temporal detection of interactions. Results We provide a modular approach in which different Artificial Intelligence temporal reasoning techniques, based on temporal constraint propagation, are widely exploited to provide users with such facilities. We applied our methodology to two cases of comorbidities, using simplified versions of CPGs. Conclusion We propose an innovative approach to the detection and analysis of interactions between CPGs considering different sources of temporal information (CPGs, ontological knowledge and execution logs), which is the first one in the literature that takes into account the temporal issues, and accounts for different application scenarios