# Algoritmos de miner'ia de datos en la industria sanitaria

arXiv: https://arxiv.org/abs/2104.09395 · HTML: https://mlsanity.com/paper/2104.09395/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 91 references that are in the map.
- First posted on arXiv 19 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning](https://mlsanity.com/paper/1803.04765/) (2018)
- [Adaptive MapReduce Similarity Joins](https://mlsanity.com/paper/1804.05615/) (2018)
- [I/O-Efficient Similarity Join](https://mlsanity.com/paper/1507.00552/) (2015)

## Abstract (arXiv)

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.