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

 
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. C 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 416–430 DOI:
10.1002/widm.23