Francesco Lupia , Enrico Russo , Giacomo Longo , Andrea Pugliese
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引用次数: 0
Abstract
Clinical Pathways (CPs) consist of structured multidisciplinary guidelines and protocols used to model steps of clinical treatments. The main objective of applying CPs is that of optimizing both outcomes and efficiency — however, the actual implementation of CPs can be complex and result in important deviations and unexpected inefficiencies. In this paper, we develop an approach to identifying and understanding such problems by leveraging process mining techniques and background knowledge. We design specific data structures aimed at properly capturing the data produced during the implementation of CPs, including the treatment of more than one disease for a single patient. We then provide a methodology to discover and characterize congestion dynamics in CPs. Since the resulting process discovery problem is theoretically intractable, we develop heuristic algorithms that, based on an extensive experimental assessment, prove capable of discovering meaningful knowledge with a reasonable computational effort.
期刊介绍:
Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory.
The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation.
This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods.
Computational science typically unifies three distinct elements:
• Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous);
• Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems;
• Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).