NEWS

  • Process Mining for Healthcare paper in Journal of Biomedical Informatics

    It’ out! Jorge Munoz-Gama, Niels Martinb, Carlos Fernandez-Llatas, Owen A. Johnson, Marcos Sepúlveda, Emmanuel Helm, Victor Galvez-Yanjari, Eric Rojas, Antonio Martinez-Millana, Davide Aloini, Ilaria Angela Amantea, Robert Andrews, Michael Arias, Iris Beerepoot, Elisabetta Benevento, Andrea Burattin, Daniel Capurro, Josep Carmona, Marco Comuzzi, Benjamin Dalmas, Rene de la Fuente, Chiara Di Francescomarino, Claudio Di Ciccio, Roberto Gatta, Chiara Ghidini, Fernanda Gonzalez-Lopez, Gema Ibanez-Sanchez, Hilda B.Klasky, Angelina Prima Kurniati, Xixi Lu, Felix Mannhardt, Ronny Mansa, Mar Marcos, Renata Medeiros de Carvalho, Marco Pegoraro, Simon K. Poon, Luise Pufahl, Hajo A.Reijers, Simon Remy, Stefanie Rinderle-Maa, Lucia Sacchi, Fernando Seoane, Minseok Song, Alessandro Stefanini, Emilio Sulis, Arthur H.M.ter Hofstede, Pieter J.Toussaint, Vicente Traver, Zoe Valero-Ramon, Inge van de Weerd, Wil M.P.van der Aalst, Rob Vanwersch, Mathias Weske, Moe Thandar Wynn, Francesca Zerbato. Process mining for healthcare: Characteristics and challenges, Journal of Biomedical Informatics, Volume 127, March 2022, 103994. https://doi.org/10.1016/j.jbi.2022.103994

    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.

  • We are part of the first Process MIning Summer School 2022

    We are part of the first Summer School on Process Mining organized by the IEEE Task Force on Process Mining will take place in Aachen, Germany from 4 to 8 July 2022 with a course on Predictive Process Monitoring!

  • Paper on Computational Persuasion at AAAI 2022

    The paper Machine Learning for Utility Prediction in Argument-Based Computational Persuasion by Ivan Donadello, Anthony Hunter, Stefano Teso, and Mauro Dragoni has been accepted for presentation at the main technical session at AAAI 2022!

    Abstract: Automated persuasion systems (APS) aim to persuade a user to believe something by entering into a dialogue in which arguments and counterarguments are exchanged. To maximize the probability that an APS is successful in persuading a user, it can identify a global policy that will allow it to select the best arguments it presents at each stage of the dialogue whatever arguments the user presents. However, in real applications, such as for healthcare, it is unlikely the utility of the outcome of the dialogue will be the same, or the exact opposite, for the APS and user. In order to deal with this situation, games in extended form have been harnessed for argumentation in Bi-party Decision Theory. This opens new problems that we address in this paper: (1) How can we use Machine Learning (ML) methods to predict utility functions for different subpopulations of users? and (2) How can we identify for a new user the best utility function from amongst those that we have learned? To this extent, we develop two ML methods, EAI and EDS, that leverage information coming from the users to predict their utilities. EAI is restricted to a fixed amount of information, whereas EDS can choose the information that best detects the subpopulations of a user. We evaluate EAI and EDS in a simulation setting and in a realistic case study concerning healthy eating habits. Results are promising in both cases, but EDS is more effective at predicting useful utility functions.

  • Paper on Reachability of Wf-nets in Expert Syst. Appl

    Its Out! Riccardo De Masellis, Chiara Di Francescomarino, Chiara Ghidini, Sergio Tessaris: Solving reachability problems on data-aware workflows. Expert Syst. Appl. 189: 116059 (2022). https://doi.org/10.1016/j.eswa.2021.116059

    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 pro- cess models, an extension of Workflow Nets. Then we provide a rigorous map- ping between the semantics of such models and that of three important Auto- mated 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.