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

Riccardo Bellazzi, Fulvia Ferrazzi, Lucia Sacchi

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)416-430
Number of pages15
JournalWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Issue number5
Publication statusPublished - 2011

ASJC Scopus subject areas

  • Computer Science(all)


Dive into the research topics of 'Predictive data mining in clinical medicine: A focus on selected methods and applications'. Together they form a unique fingerprint.

Cite this