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Predictive data mining in clinical medicine: Current issues and guidelines
Open accessAbstract
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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“This approach is getting more and more interest for the healthcare providers in the medical domain since it helps to prevent further chronic problems [18] and could lead to a decision about prognosis [37].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [37]
In the citing paper
“Due to the admissible predictive performance of NN, it is presently the most popular data modeling method used in the medical domain [37].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [37]
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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)
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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