Matteo Spiotta

Business process verification with constraint temporal answer set programming

The paper provides a framework for the verication of business processes, based on an extension of answer set programming (ASP) with temporal logic and constraints. The framework allows to capture expressive uent annotations as well as data awareness in a uniform way. It allows for a declarative specication of a business process but also for encoding processes specied in conventional workow languages. Verication of temporal properties of a business process, including verication of compliance to business rules, is performed by bounded model checking techniques in Answer Set Programming, extended with constraint solving for dealing with conditions on numeric data.

Answer Set Programming for Temporal Conformance Analysis of Clinical Guidelines Execution

Analyzing conformance of execution traces with clinical guidelines is not a trivial task, because guidelines are developed for physicians who should always interpret them according to their general knowledge; their application to the specific conditions of a patient or a specific context cannot always be foreseen in the guideline specification. In this paper we consider the conformance problem not only for the sequence of action execution events, but also for their timing: the guideline may include temporal constraints for the execution of actions, and its adaptation to a specific patient and context may add or modify conditions and temporal constraints for actions. We propose an approach for analyzing execution traces in Answer Set Programming with respect to a guideline and Basic Medical Knowledge, pointing out discrepancies --- including temporal discrepancies --- with respect to the different knowledge sources.

Temporal Conformance Analysis and Explanation of Clinical Guidelines Execution: An Answer Set Programming Approach

Clinical Guidelines (CGs) provide general evidence-based recommendations and physicians often have to resort also to their Basic Medical Knowledge (BMK) to cope with specific patients. In this paper, we explore the interplay between CGs and BMK from the viewpoint of a-posteriori conformance analysis, intended as the adherence of a specific execution log to both the CG and the BMK. In this paper, we consider also the temporal dimension: the guideline may include temporal constraints for the execution of actions, and its adaptation to a specific patient and context may add or modify conditions and temporal constraints for actions. We propose an approach for analyzing execution traces in Answer Set Programming with respect to a guideline and BMK, pointing out discrepancies – including temporal discrepancies – with respect to the different knowledge sources, and providing explanations regarding how the applications of the CG and the BMK have interacted, especially in case strictly adhering to both is not possible.

ASP for Conformance Analysis and Explanation of Clinical Guidelines Execution

In this paper, we present an approach where Answer Set Programming is used for analyzing the conformance of execution traces, describing the treatment of individual patients, to Computer-Interpretable Guidelines (CIGs) in the medical domain. The problem is challenging because the CIG for a given pathology describes the most typical treatments for that pathology and cannot take into account all the specific cases that may occur for a specific patient, being them contraindications, temporary conditions of the patient, or other major pathologies. Deviations (also in the timing) from a single guideline may then be explainable using general medical knowledge or knowledge about the interaction between pathologies or treatments.

Temporal Conformance Analysis of Clinical Guidelines Execution

Physicians often have to combine clinical guideline recommendations with their own basic medical knowledge to cope with specific patients in specific contexts. Both knowledge sources may include temporal constraints for the execution of actions. In this paper we approach the problem of compliance analysis with both sources of knowledge, pointing out discrepancies – including temporal ones – with respect to them, and where such discrepancies may be due to multiple and possibly conflicting recommendations.