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1.
  • Munoz-Gama, Jorge, et al. (författare)
  • Process mining for healthcare : Characteristics and challenges
  • 2022
  • Ingår i: Journal of Biomedical Informatics. - : Elsevier BV. - 1532-0464 .- 1532-0480. ; 127
  • Tidskriftsartikel (refereegranskat)abstract
    • Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.
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2.
  • De Masellis, Riccardo, et al. (författare)
  • Solving reachability problems on data-aware workflows
  • 2022
  • Ingår i: Expert systems with applications. - : Elsevier. - 0957-4174 .- 1873-6793. ; 189
  • Tidskriftsartikel (refereegranskat)abstract
    • Recent advances in the field of Business Process Management (BPM) have brought about several suites able to model data objects along with the traditional control flow perspective. Nonetheless, when it comes to formal verification there is still a lack of effective verification tools on imperative data-aware process models and executions: the data perspective is often abstracted away and verification tools are often missing.Automated Planning is one of the core areas of Artificial Intelligence where theoretical investigations and concrete and robust tools have made possible the reasoning about dynamic systems and domains. Moreover planning techniques are gaining popularity in the context of BPM. Starting from these observations, we provide here a concrete framework for formal verification of reachability properties on an expressive, yet empirically tractable class of data-aware process models, an extension of Workflow Nets. Then we provide a rigorous mapping between the semantics of such models and that of three important Automated Planning paradigms: Action Languages, Classical Planning, and Model-Checking. Finally, we perform a comprehensive assessment of the performance of three popular tools supporting the above paradigms in solving reachability problems for imperative data-aware business processes, which paves the way for a theoretically well founded and practically viable exploitation of planning-based techniques on data-aware business processes.
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