Algoritmos de minería de datos en la industria sanitaria
This is an incremental review that addresses the computational challenges of processing diverse health data types like ECG and fMRI using parallel algorithms.
The paper reviews data mining approaches for health applications, focusing on hardware-centric methods that exploit parallelism in modern processors and GPUs to handle large-scale medical data.
In this paper, we review data mining approaches for health applications. Our focus is on hardware-centric approaches. Modern computers consist of multiple processors, each equipped with multiple cores, each with a set of arithmetic/logical units. Thus, a modern computer may be composed of several thousand units capable of doing arithmetic operations like addition and multiplication. Graphic processors, in addition may offer some thousand such units. In both cases, single instruction multiple data and multiple instruction multiple data parallelism must be exploited. We review the principles of algorithms which exploit this parallelism and focus also on the memory issues when multiple processing units access main memory through caches. This is important for many applications of health, such as ECG, EEG, CT, SPECT, fMRI, DTI, ultrasound, microscopy, dermascopy, etc.