Predictive data mining in clinical medicine: a focus on selected methods and applications

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