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WIREs Data Mining Knowl Discov
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Predictive data mining in clinical medicine: a focus on selected methods and applications

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Abstract Predictive data mining in clinical medicine deals with learning models to predict patients' health. The models can be devoted to support clinicians in diagnostic, therapeutic, or monitoring tasks. Data mining methods are usually applied in clinical contexts to analyze retrospective data, thus giving healthcare professionals the opportunity to exploit large amounts of data routinely collected during their day‐by‐day activity. Moreover, clinicians can nowadays take advantage of data mining techniques to deal with the huge amount of research results obtained by molecular medicine, such as genetic or genomic signatures, which may allow transition from population‐based to personalized medicine. The current challenge is to exploit data mining to build models able to take into account the dynamic and temporal nature of clinical care and to exploit the variety of information available at the bedside. This review describes the main features of predictive clinical data mining and focus on two specific aspects of particular interest: the methods able to deal with temporal data and the efforts performed to translate molecular medicine results into clinically useful data mining models. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 416–430 DOI: 10.1002/widm.23 This article is categorized under: Application Areas > Health Care Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Technologies > Prediction

Biomedical informatics is a discipline with several domains of applications. It deals with diverse data sources including molecular and cellular processes, tissues and organs, and individual patients and populations (adapted from Ref 1). Data mining can be successfully applied in all areas to support decision‐making activities.

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Learning clinical predictive models requires a careful evaluation process. Different data sources need to be properly integrated and preprocessing and feature selection may turn out to be the most important parts of data analysis. Model evaluation requires an independent data set to assess the prediction performance. Finally, the model should be deployed carefully taking into account the clinical context.

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Clinical decision‐making is largely based on results coming from clinical trials. The analysis of clinical databases performed with data mining approaches provides a way to generate hypotheses for further trials and suggest changes in day‐by‐day practice based on the accumulated experience.

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Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining
Technologies > Prediction
Application Areas > Health Care

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