Yearb Med Inform 2009; 18(01): 121-133
DOI: 10.1055/s-0038-1638651
Original Article
Georg Thieme Verlag KG Stuttgart

Clinical Data Mining: a Review

J. Iavindrasana
1  University and Hospitals of Geneva, Switzerland
,
G. Cohen
1  University and Hospitals of Geneva, Switzerland
,
A. Depeursinge
1  University and Hospitals of Geneva, Switzerland
,
H. Müller
1  University and Hospitals of Geneva, Switzerland
,
R. Meyer
1  University and Hospitals of Geneva, Switzerland
,
A. Geissbuhler
1  University and Hospitals of Geneva, Switzerland
› Institutsangaben
Weitere Informationen

Correspondence to

Jimison Iavindrasana
Division of Medical Informatics
University Hospitals and University of Geneva Rue Gabrielle-Perret-Gentil 4
CH-1211Geneva 14
Switzerland
Telefon: +41 22 372 88 74   
Fax: +41 22 372 88 79   

Publikationsverlauf

Publikationsdatum:
07. März 2018 (online)

 

Summary

Objective Clinical data mining is the application of data mining techniques using clinical data. We review the literature in order to provide a general overview by identifying the status-of-practice and the challenges ahead.

Methods The nine data mining steps proposed by Fayyad in 1996 [4] were used as the main themes of the review. MEDLINE was used as primary source and 84 papers were retained based on our inclusion criteria.

Results Clinical data mining has three objectives: understanding the clinical data, assist healthcare professionals, and develop a data analysis methodology suitable for medical data. Classification is the most frequently used data mining function with a predominance of the implementation of Bayesian classifiers, neural networks, and SVMs (Support Vector Machines). A myriad of quantitative performance measures were proposed with a predominance of accuracy, sensitivity, specificity, and ROC curves. The latter are usually associated with qualitative evaluation.

Conclusion Clinical data mining respects its commitment to extracting new and previously unknown knowledge from clinical databases. More efforts are still needed to obtain a wider acceptance from the healthcare professionals and for generalization of the knowledge and reproducibility of its extraction process: better description of variables, systematic report of algorithm parameters including the method to obtain them, use of easy-to-understand models and comparisons of the efficiency of clinical data mining with traditional statistical analyses. More and more data will be available for data miners and they have to develop new methodologies and infrastructures to analyze the increasingly complex medical data.


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Correspondence to

Jimison Iavindrasana
Division of Medical Informatics
University Hospitals and University of Geneva Rue Gabrielle-Perret-Gentil 4
CH-1211Geneva 14
Switzerland
Telefon: +41 22 372 88 74   
Fax: +41 22 372 88 79