A knowledge-intensive approach to process similarity calculation

Process model comparison and similar processes retrieval are key issues to be addressed in many real world situations, and particularly relevant ones in some applications (e.g., in medicine), where similarity quantification can be exploited in a quality assessment perspective. Most of the process comparison techniques described in the literature suffer from two main limitations: (1) they adopt a purely syntactic (vs. semantic) approach in process activity comparison, and/or (2) they ignore complex control flow information (i.e., other than sequence). These limitations oversimplify the problem, and make the results of similarity-based process retrieval less reliable, especially when domain knowledge is available, and can be adopted to quantify activity or control flow construct differences. In this paper, we aim at overcoming both limitations, by introducing a framework which allows to extract the actual process model from the available process execution traces, through process mining techniques, and then to compare (mined) process models, by relying on a novel distance measure. The novel distance measure, which represents the main contribution of this paper, is able to address issues (1) and (2) above, since: (1) it provides a semantic, knowledge-intensive approach to process activity comparison, by making use of domain knowledge; (2) it explicitly takes into account complex control flow constructs (such as AND and XOR splits/joins), thus fully considering the different semantic meaning of control flow connections in a reliable way. The positive impact of the framework in practice has been tested in stroke management, where our approach has outperformed a state-of-the art literature metric on a real world event log, providing results that were closer to those of a human expert. Experiments in other domains are foreseen in the future

DBNet, a tool to convert Dynamic Fault Trees into Dynamic Bayesian Networks

The unreliability evaluation of a system including dependencies involving the state of components or the failure events, can be performed by modelling the system as a Dynamic Fault Tree (DFT). The combinatorial technique used to solve standard Fault Trees is not suitable for the analysis of a DFT. The conversion into a Dynamic Bayesian Network (DBN) is a way to analyze a DFT. This paper presents a software tool allowing the automatic analysis of a DFTexploiting its conversion to a DBN. First, the architecture of the tool is described, together with the rules implemented in the tool, to convert dynamic gates in DBNs. Then, the tool is tested on a case of system: its DFT model and the corresponding DBN are provided and analyzed by means of the tool. The obtained unreliability results are compared with those returned by other tools, in order to verify their correctness. Moreover, the use of DBNs allows to compute further results on the model, such as diagnostic and sensitivity indices

Hepatic manifestations of drug reaction with eosinophilia and systemic symptoms syndrome

Drug reaction with eosinophilia and systemic symptoms (DRESS) syndrome is a potentially life-threatening drug reaction, which can affect multiple organs. Patients with DRESS syndrome and hepatic manifestations may present alterations ranging from mild hepatitis to acute liver failure. The diagnosis might be difficult, and the management of these patients is challenging. This report analyzes a series of five cases reporting the clinical presentation, which ranged from acute hepatitis to liver failure, and discussed their treatment

Case-based Reasoning for managing non-compliance with clinical guidelines

Abstract. Despite the recognized advantages that can be obtained in clinical practice when following clinical guidelines (GL), situations of non-compliance with them may emerge. Keeping track of such deviations from the default GL execution, and documenting the physician’s motivations, would clearly be an added value. Moreover, repeated alterations of GL tasks (or flow) may indicate an improper or weak initial GL definition, and might be used as a starting point for suggesting a formal GL revision to a committee of expert physicians. In this paper, we propose an approach for managing non-compliance with GL, based on the Case-based Reasoning methodology. In front of a new non-compliance case, our tool allows the physician to retrieve past situations similar to the current one, and to decide whether to re-apply the same GL modifications adopted in them. Moreover, the tool is able to learn indications from the ground non-compliance cases, that can be deployed to suggest GL revisions. In particular, the issue of supporting GL revision to our knowledge has never been systematically treated.