Predictive data mining in clinical medicine: Current issues and guidelines

BELLAZZI, R; ZUPAN, B

doi:10.1016/j.ijmedinf.2006.11.006

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

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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Predictive data mining is getting more interest from healthcare providers because it helps prevent further chronic problems and can lead to prognosis decisions.
“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]
Due to admissible predictive performance, neural networks are presently the most popular data modeling method used in the medical domain.
“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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Untitled work
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What do we mean by validating a prognostic model?
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VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY
BRIER, G. W. (1950) · Monthly Weather Review · doi:10.1175/1520-0493(1950)078
Verification of forecasts expressed in terms of probability
BRIER, G. W. (1950) · Monthly Weather Review · doi:10.1175/1520-0493(1950)078%3c0001:vofeit%3e2.0.co;2 · cited 2×
“[92], area under the ROC curve [93] and Brier score [94].”
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Type 2 diabetes mellitus prediction model based on data mining
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Assessment of Landslide Susceptibility by Decision Trees in the Metropolitan Area of Istanbul, Turkey
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“Big Data” and the Electronic Health Record
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Use of Data Mining Techniques to Determine and Predict Length of Stay of Cardiac Patients
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A review of causal inference for biomedical informatics
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Machine-Learning Algorithms Predict Graft Failure After Liver Transplantation
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A manifesto on explainability for artificial intelligence in medicine
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Prediction and decision making in Health Care using Data Mining
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A review of statistical and machine learning methods for modeling cancer risk using structured clinical data
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Magnetic resonance imaging pattern learning in temporal lobe epilepsy: Classification and prognostics
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Efficient and Privacy-Preserving Online Medical Prediagnosis Framework Using Nonlinear SVM
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EEG-Based Prediction of Epileptic Seizures Using Phase Synchronization Elicited from Noise-Assisted Multivariate Empirical Mode Decomposition
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Predicting the graft survival for heart–lung transplantation patients: An integrated data mining methodology
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Health informatics: current issues and challenges
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Identifying child abuse through text mining and machine learning
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Machine Learning for Survival Analysis: A SurveyPreprint
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Using data mining techniques to predict hospitalization of hemodialysis patients
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Rethinking construction cost overruns: cognition, learning and estimation
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Investigating the Heart Pump Implant Decision Process: Opportunities for Decision Support Tools to Help
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New Machine Learning Approach for Detection of Injury Risk Factors in Young Team Sport Athletes
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AI in Rehabilitation Medicine: Opportunities and Challenges
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A machine learning-based approach to prognostic analysis of thoracic transplantations
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Using EHR data to predict hospital-acquired pressure ulcers: A prospective study of a Bayesian Network model
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Comparison of Models for the Prediction of Medical Costs of Spinal Fusion in Taiwan Diagnosis-Related Groups by Machine Learning Algorithms
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Machine Learning-Based Missing Value Imputation Method for Clinical Datasets
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Authors: BELLAZZI, R., & ZUPAN, B.
Affiliations: University of Pavia (IT)
University of Ljubljana (SI)
Corresponding author: BELLAZZI, R
ORCID: BELLAZZI, R
ZUPAN, B
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: green
repozitorij.uni-lj.si
Licence: other-oa
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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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Predictive data mining in clinical medicine: Current issues and guidelines

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