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Cited by 823Artificial Intelligence in HealthcareMachine Learning in HealthcareArtificial Intelligence in Healthcare and Education

The DOI matches its Crossref record; 109 of the 111 references it cites were found in a registry or an index.

Predictive data mining in clinical medicine: Current issues and guidelines

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BELLAZZI, R, ZUPAN, B·2008·International Journal of Medical Informatics
DOI 10.1016/j.ijmedinf.2006.11.006
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Abstract

Background The widespread availability of new computational methods and tools for data analysis and predictive modeling requires medical informatics researchers and practitioners to systematically select the most appropriate strategy to cope with clinical prediction problems. In particular, the collection of methods known as 'data mining' offers methodological and technical solutions to deal with the analysis of medical data and construction of prediction models. A large variety of these methods requires general and simple guidelines that may help practitioners in the appropriate selection of data mining tools, construction and validation of predictive models, along with the dissemination of predictive models within clinical environments.Purpose The goal of this review is to discuss the extent and role of the research area of predictive data mining and to propose a framework to cope with the problems of constructing, assessing and exploiting data mining models in clinical medicine.Methods We review the recent relevant work published in the area of predictive data mining in clinical medicine, highlighting critical issues and summarizing the approaches in a set of learned lessons.Results The paper provides a comprehensive review of the state of the art of predictive data mining in clinical medicine and gives guidelines to carry out data mining studies in this field.Conclusions Predictive data mining is becoming an essential instrument for researchers and clinical practitioners in medicine. Understanding the main issues underlying these methods and the application of agreed and standardized procedures is mandatory for their deployment and the dissemination of results. Thanks to the integration of molecular and clinical data taking place within genomic medicine, the area has recently not only gained a fresh impulse but also a new set of complex problems it needs to address.

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Metadata

Authors
BELLAZZI, R., & ZUPAN, B.
Affiliations
  • University of Pavia (IT)
  • University of Ljubljana (SI)
Corresponding author
BELLAZZI, R
ORCID
Venue
International Journal of Medical Informatics
Publisher
Elsevier BV
Published
2008-02
Volume
77
Issue
2
Pages
81-97
ISSN
1386-5056 (ISSN-L)
DOI
10.1016/j.ijmedinf.2006.11.006
OpenAlex
W2132996843
Cited by
823
References
111
Citations by year
2026: 19 · 2025: 20 · 2024: 43 · 2023: 37 · 2022: 41 · 2021: 44 · 2020: 62 · 2019: 56 · 2018: 64 · 2017: 76 · 2016: 85 · 2015: 59 · 2014: 46 · 2013: 53 · 2012: 35
FWCI
37.51
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Medical Informatics (publishedVersion)
  • PubMed (publishedVersion)
  • Repository of the University of Ljubljana (University of Ljubljana) (publishedVersion, other-oa)
Topics
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
predictive data mining, clinical prediction model, model validation, model dissemination, clinical decision support, genomic medicine, methodological recommendations, medical informatics
MeSH terms
Statistics as Topic, United States, Models, Statistical, Clinical Medicine, Guidelines as Topic

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