Artificial Intelligence in Medicine 

TL;DR: An overview of the development of intelligent data analysis in medicine from a machine learning perspective: a historical view, a state-of-the-art view, and a view on some future trends in this subfield of applied artificial intelligence.

TL;DR: The comparative study of multiple prediction models for breast cancer survivability using a large dataset along with a 10-fold cross-validation provided us with an insight into the relative prediction ability of different data mining methods.

TL;DR: The current research in wearable is examined to serve as references for researchers and provide perspectives for future research, focusing on multi-parameter physiological sensor systems and activity and mobility measurement system designs that reliably measure mobility or vital signs and integrate real-time decision support processing for disease prevention, symptom detection, and diagnosis.

TL;DR: Ethical and legal aspects of medical data mining are discussed, including data ownership, fear of lawsuits, expected benefits, and special administrative issues.

TL;DR: A novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data is presented using a kernel-induced distance metric and a spatial penalty on the membership functions to compensate for the intensity inhomogeneities of MR image.