# DM algorithms in health industry

arXiv: https://arxiv.org/abs/2103.01888 · HTML: https://mlsanity.com/paper/2103.01888/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 90 references that are in the map.
- First posted on arXiv 2 Mar 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)

This survey reviews several approaches of data mining (DM) in healthindustry from many research groups world wide. The focus is on modern multi-core processors built into today's commodity computers, which are typically found at university institutes both as small server and workstation computers. So they are deliberately not high-performance computers. Modern multi-core processors consist of several (2 to over 100) computer cores, which work independently of each other according to the principle of "multiple instruction multiple data" (MIMD). They have a common main memory (shared memory). Each of these computer cores has several (2-16) arithmetic-logic units, which can simultaneously carry out the same arithmetic operation on several data in a vector-like manner (single instruction multiple data, SIMD). DM algorithms must use both types of parallelism (SIMD and MIMD), with access to the main memory (centralized component) being the main barrier to increased efficiency. This is important for DM in healthindustry applications like ECG, EEG, CT, SPECT, fMRI, DTI, ultrasound, microscopy, dermascopy, etc.