A Framework for Event Log Generation and Knowledge Representation for Process Mining in Healthcare

Roberto Gatta, Mauro Vallati, Jacopo Lenkowicz, Calogero Casà, Francesco Cellini, Andrea Damiani, Vincenzo Valentini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Process Mining is of growing importance in the healthcare domain, where the quality of delivered services depends on the suitable and efficient execution of processes encoding the vast amount of clinical knowledge gained via the evidence-based medicine paradigm. In particular, to assess and measure the quality of delivered treatments, there is a strong interest in tools able to perform conformance checking.
In process mining for the healthcare domain, a number of major challenges are posed by: (i) the complexity of involved data, that refers to patients’ aspects such as disease, behaviour, clinical history, psychology, etc; (ii) the availability of data, that come from the heterogeneous, fragmented and scant connected healthcare system; and (iii) the wide range of available standards for communication (DICOM, IHE, etc.) or data representation (ICD9, SNOMED, etc.) purposes.
To effectively perform process mining in the healthcare domain, it is crucial to build event logs capturing all the steps of running processes, which have to be derived by the knowledge stored in the Electronic Health Records. It is therefore crucial to cope with aforementioned data-related challenges.
In this paper, we aim at supporting the exploitation of process mining in the healthcare domain, particularly with regards to conformance checking. We therefore introduce a set of specifically-designed techniques, provided as a suite of software packages written in R. In particular, the suite provides a flexible and agile way to automatically and reliably build Event Log from clinical data sources, and to effectively perform conformance checking.
Original languageEnglish
Title of host publication30th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2018)
PublisherIEEE
Pages647-654
Number of pages8
ISBN (Electronic)9781538674499
ISBN (Print)9781538674505
DOIs
Publication statusPublished - 13 Dec 2018
Event30th IEEE International Conference on Tools with Artificial Intelligence - Volos, Greece
Duration: 5 Nov 20187 Nov 2018
Conference number: 30
http://ictai2018.org/ (Link to Conference Website)

Conference

Conference30th IEEE International Conference on Tools with Artificial Intelligence
Abbreviated titleICTAI 2018
CountryGreece
CityVolos
Period5/11/187/11/18
Internet address

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  • Cite this

    Gatta, R., Vallati, M., Lenkowicz, J., Casà, C., Cellini, F., Damiani, A., & Valentini, V. (2018). A Framework for Event Log Generation and Knowledge Representation for Process Mining in Healthcare. In 30th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2018) (pp. 647-654). [8576101] IEEE. https://doi.org/10.1109/ICTAI.2018.00103