Paper Info
Title
A Framework For Event Log Generation And Knowledge Representation For Process Mining In Healthcare
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.
Year
DOI
Venue
2018
10.1109/ICTAI.2018.00103
2018 IEEE 30TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI)
Keywords
Field
DocType
Process Mining, Healthcare, Conformance, Checking
Data science,Knowledge representation and reasoning,External Data Representation,DICOM,Work in process,Computer science,Agile software development,Artificial intelligence,Conformance checking,SNOMED CT,Machine learning,Process mining
Conference
ISSN
Citations 
PageRank 
1082-3409
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
R Gatta174.70
Mauro Vallati221646.63
Jacopo Lenkowicz300.34
Calogero Casá400.68
Francesco Cellini541.82
Andrea Damiani631.83
Vincenzo Valentini732.17