Process mining and data mining applications in the domain of chronic diseases: A systematic review.

Chronic disease Data mining Process mining Systematic review

Journal

Artificial intelligence in medicine
ISSN: 1873-2860
Titre abrégé: Artif Intell Med
Pays: Netherlands
ID NLM: 8915031

Informations de publication

Date de publication:
10 2023
Historique:
received: 02 03 2023
revised: 24 08 2023
accepted: 28 08 2023
medline: 4 10 2023
pubmed: 3 10 2023
entrez: 2 10 2023
Statut: ppublish

Résumé

The widespread use of information technology in healthcare leads to extensive data collection, which can be utilised to enhance patient care and manage chronic illnesses. Our objective is to summarise previous studies that have used data mining or process mining methods in the context of chronic diseases in order to identify research trends and future opportunities. The review covers articles that pertain to the application of data mining or process mining methods on chronic diseases that were published between 2000 and 2022. Articles were sourced from PubMed, Web of Science, EMBASE, and Google Scholar based on predetermined inclusion and exclusion criteria. A total of 71 articles met the inclusion criteria and were included in the review. Based on the literature review results, we detected a growing trend in the application of data mining methods in diabetes research. Additionally, a distinct increase in the use of process mining methods to model clinical pathways in cancer research was observed. Frequently, this takes the form of a collaborative integration of process mining, data mining, and traditional statistical methods. In light of this collaborative approach, the meticulous selection of statistical methods based on their underlying assumptions is essential when integrating these traditional methods with process mining and data mining methods. Another notable challenge is the lack of standardised guidelines for reporting process mining studies in the medical field. Furthermore, there is a pressing need to enhance the clinical interpretation of data mining and process mining results.

Identifiants

pubmed: 37783545
pii: S0933-3657(23)00159-8
doi: 10.1016/j.artmed.2023.102645
pii:
doi:

Types de publication

Systematic Review Journal Article Review Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

102645

Informations de copyright

Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest Farhad Abtahi is the founder and CEO of Wergonic AB. Wergonic AB is a start-up company developing digital solutions for precision ergonomics.

Auteurs

Kaile Chen (K)

Department of Clinical Science, Intervention and Technology, Karolinska Institutet, 17177 Stockholm, Sweden; School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Biomedical Engineering and Health Systems, Division of Ergonomics, KTH Royal Institute of Technology, 14157 Stockholm, Sweden. Electronic address: kaile.chen@ki.se.

Farhad Abtahi (F)

Department of Clinical Science, Intervention and Technology, Karolinska Institutet, 17177 Stockholm, Sweden; School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Biomedical Engineering and Health Systems, Division of Ergonomics, KTH Royal Institute of Technology, 14157 Stockholm, Sweden; Department of Clinical Physiology, Karolinska University Hospital, 17176 Stockholm, Sweden.

Juan-Jesus Carrero (JJ)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden.

Carlos Fernandez-Llatas (C)

Department of Clinical Science, Intervention and Technology, Karolinska Institutet, 17177 Stockholm, Sweden; SABIEN, ITACA, Universitat Politècnica de València, Spain.

Fernando Seoane (F)

Department of Clinical Science, Intervention and Technology, Karolinska Institutet, 17177 Stockholm, Sweden; Department of Clinical Physiology, Karolinska University Hospital, 17176 Stockholm, Sweden; Department of Medical Technology, Karolinska University Hospital, 17176 Stockholm, Sweden; Department of Textile Technology, University of Borås, 50190 Borås, Sweden.

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