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Process mining and data mining applications in the domain of chronic diseases : A systematic review
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- Chen, Kaile (författare)
- KTH,Ergonomi,Department of Clinical Science, Intervention and Technology, Karolinska Institute, 17177 Stockholm, Sweden
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- Abtahi, Farhad, 1981- (författare)
- Karolinska Institutet,KTH,Ergonomi,Department of Clinical Science, Intervention and Technology, Karolinska Institute, 17177 Stockholm, Sweden; Department of Clinical Physiology, Karolinska University Hospital, 17176 Stockholm, Sweden
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- Carrero, Juan Jesus (författare)
- Karolinska Institutet
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- Fernandez-Llatas, Carlos (författare)
- Department of Clinical Science, Intervention and Technology, Karolinska Institute, 17177 Stockholm, Sweden; SABIEN, ITACA, Universitat Politecnica de Valencia, Valencia, Spain
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- Seoane, Fernando, 1976- (författare)
- Högskolan i Borås,Akademin för textil, teknik och ekonomi,Akademin för vård, arbetsliv och välfärd
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(creator_code:org_t)
- Elsevier BV, 2023
- 2023
- Engelska.
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Ingår i: Artificial Intelligence in Medicine. - : Elsevier BV. - 0933-3657 .- 1873-2860. ; 144
- Relaterad länk:
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https://doi.org/10.1...
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https://hb.diva-port... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Annan samhällsbyggnadsteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Other Civil Engineering (hsv//eng)
Nyckelord
- Chronic disease
- Data mining
- Process mining
- Systematic review
Publikations- och innehållstyp
- ref (ämneskategori)
- for (ämneskategori)
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