Newsletter Downloads
Introduction to the special section on clinical data mining
Mining clinical data is a fast-evolving field, ranging from mining patient data of a particular type (e.g., images, genomics) to mining the increased amount of mixed-format information (databases, free text, images, labs, etc) in electronic health ...
Sparse methods for biomedical data
Following recent technological revolutions, the investigation of massive biomedical data with growing scale, diversity, and complexity has taken a center stage in modern data analysis. Although complex, the underlying representations of many biomedical ...
Supervised patient similarity measure of heterogeneous patient records
Patient similarity assessment is an important task in the context of patient cohort identif cation for comparative effectiveness studies and clinical decision support applications. The goal is to derive clinically meaningful distance metric to measure ...
Mining anatomical, physiological and pathological information from medical images
The field of medical imaging has shown substantial growth over the last decade. Even more dramatic increase was observed in the use of machine learning and data mining techniques within this field. In this paper, we discuss three aspects related to ...
Data mining methodologies for pharmacovigilance
Medicines are designed to cure, treat, or prevent diseases; however, there are also risks in taking any medicine - particularly short term or long term adverse drug reactions (ADRs) can cause serious harm to patients. Adverse drug events have been ...
Cross domain similarity mining: research issues and potential applications including supporting research by analogy
This paper defines the cross domain similarity mining (CDSM) problem, and motivates CDSM with several potential applications. CDSM has big potential in (1) supporting understanding transfer and (2) supporting research by analogy, since similarity is ...
Next challenges for adaptive learning systems
Learning from evolving streaming data has become a 'hot' research topic in the last decade and many adaptive learning algorithms have been developed. This research was stimulated by rapidly growing amounts of industrial, transactional, sensor and other ...