Leveraging Semantic Labels for Multi-level Abstraction in Medical Process Mining and Trace Comparison

Giorgio Leonardi, Manuel Striani, Silvana Quaglini, Anna Cavallini, Stefania Montani

Research output: Contribution to journalArticle

Abstract

Many medical information systems record data about the executed process instances in the form of an event log. In this paper, we present a framework, able to convert actions in the event log into higher level concepts, at different levels of abstraction, on the basis of domain knowledge. Abstracted traces are then provided as an input to trace comparison and semantic process discovery. Our abstraction mechanism is able to manage non trivial situations, such as interleaved actions or delays between two actions that abstract to the same concept. Trace comparison resorts to a similarity metric able to take into account abstraction phase penalties, and to deal with quantitative and qualitative temporal constraints in abstracted traces. As for process discovery, we rely on classical algorithms embedded in the framework ProM, made semantic by the capability of abstracting the actions on the basis of their conceptual meaning. The approach has been tested in stroke care, where we adopted abstraction and trace comparison to cluster event logs of different stroke units, to highlight (in)correct behavior, abstracting from details. We also provide process discovery results, showing how the abstraction mechanism allows to obtain stroke process models more easily interpretable by neurologists.

Original languageEnglish
Pages (from-to)10-24
Number of pages14
JournalJournal of Biomedical Informatics
Volume83
Early online dateMay 21 2018
DOIs
Publication statusPublished - May 21 2018

Fingerprint

Semantics
Labels
Stroke
Medical information systems
Information Systems

Cite this

Leveraging Semantic Labels for Multi-level Abstraction in Medical Process Mining and Trace Comparison. / Leonardi, Giorgio; Striani, Manuel; Quaglini, Silvana; Cavallini, Anna; Montani, Stefania.

In: Journal of Biomedical Informatics, Vol. 83, 21.05.2018, p. 10-24.

Research output: Contribution to journalArticle

Leonardi, Giorgio ; Striani, Manuel ; Quaglini, Silvana ; Cavallini, Anna ; Montani, Stefania. / Leveraging Semantic Labels for Multi-level Abstraction in Medical Process Mining and Trace Comparison. In: Journal of Biomedical Informatics. 2018 ; Vol. 83. pp. 10-24.
@article{f6d7ec5faa874b09966e2a8560dfb00f,
title = "Leveraging Semantic Labels for Multi-level Abstraction in Medical Process Mining and Trace Comparison",
abstract = "Many medical information systems record data about the executed process instances in the form of an event log. In this paper, we present a framework, able to convert actions in the event log into higher level concepts, at different levels of abstraction, on the basis of domain knowledge. Abstracted traces are then provided as an input to trace comparison and semantic process discovery. Our abstraction mechanism is able to manage non trivial situations, such as interleaved actions or delays between two actions that abstract to the same concept. Trace comparison resorts to a similarity metric able to take into account abstraction phase penalties, and to deal with quantitative and qualitative temporal constraints in abstracted traces. As for process discovery, we rely on classical algorithms embedded in the framework ProM, made semantic by the capability of abstracting the actions on the basis of their conceptual meaning. The approach has been tested in stroke care, where we adopted abstraction and trace comparison to cluster event logs of different stroke units, to highlight (in)correct behavior, abstracting from details. We also provide process discovery results, showing how the abstraction mechanism allows to obtain stroke process models more easily interpretable by neurologists.",
author = "Giorgio Leonardi and Manuel Striani and Silvana Quaglini and Anna Cavallini and Stefania Montani",
note = "Copyright {\circledC} 2018. Published by Elsevier Inc.",
year = "2018",
month = "5",
day = "21",
doi = "10.1016/j.jbi.2018.05.012",
language = "English",
volume = "83",
pages = "10--24",
journal = "Journal of Biomedical Informatics",
issn = "1532-0464",
publisher = "Academic Press Inc.",

}

TY - JOUR

T1 - Leveraging Semantic Labels for Multi-level Abstraction in Medical Process Mining and Trace Comparison

AU - Leonardi, Giorgio

AU - Striani, Manuel

AU - Quaglini, Silvana

AU - Cavallini, Anna

AU - Montani, Stefania

N1 - Copyright © 2018. Published by Elsevier Inc.

PY - 2018/5/21

Y1 - 2018/5/21

N2 - Many medical information systems record data about the executed process instances in the form of an event log. In this paper, we present a framework, able to convert actions in the event log into higher level concepts, at different levels of abstraction, on the basis of domain knowledge. Abstracted traces are then provided as an input to trace comparison and semantic process discovery. Our abstraction mechanism is able to manage non trivial situations, such as interleaved actions or delays between two actions that abstract to the same concept. Trace comparison resorts to a similarity metric able to take into account abstraction phase penalties, and to deal with quantitative and qualitative temporal constraints in abstracted traces. As for process discovery, we rely on classical algorithms embedded in the framework ProM, made semantic by the capability of abstracting the actions on the basis of their conceptual meaning. The approach has been tested in stroke care, where we adopted abstraction and trace comparison to cluster event logs of different stroke units, to highlight (in)correct behavior, abstracting from details. We also provide process discovery results, showing how the abstraction mechanism allows to obtain stroke process models more easily interpretable by neurologists.

AB - Many medical information systems record data about the executed process instances in the form of an event log. In this paper, we present a framework, able to convert actions in the event log into higher level concepts, at different levels of abstraction, on the basis of domain knowledge. Abstracted traces are then provided as an input to trace comparison and semantic process discovery. Our abstraction mechanism is able to manage non trivial situations, such as interleaved actions or delays between two actions that abstract to the same concept. Trace comparison resorts to a similarity metric able to take into account abstraction phase penalties, and to deal with quantitative and qualitative temporal constraints in abstracted traces. As for process discovery, we rely on classical algorithms embedded in the framework ProM, made semantic by the capability of abstracting the actions on the basis of their conceptual meaning. The approach has been tested in stroke care, where we adopted abstraction and trace comparison to cluster event logs of different stroke units, to highlight (in)correct behavior, abstracting from details. We also provide process discovery results, showing how the abstraction mechanism allows to obtain stroke process models more easily interpretable by neurologists.

U2 - 10.1016/j.jbi.2018.05.012

DO - 10.1016/j.jbi.2018.05.012

M3 - Article

C2 - 29793072

VL - 83

SP - 10

EP - 24

JO - Journal of Biomedical Informatics

JF - Journal of Biomedical Informatics

SN - 1532-0464

ER -