# Medical Pathologies Prediction : Systematic Review and Proposed Approach

arXiv: https://arxiv.org/abs/2304.00311 · HTML: https://mlsanity.com/paper/2304.00311/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 52 references that are in the map.
- First posted on arXiv 1 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records](https://mlsanity.com/paper/1903.09296/) (2019)
- [Unsupervised Machine Learning for the Discovery of Latent Disease Clusters and Patient Subgroups Using Electronic Health Records](https://mlsanity.com/paper/1905.10309/) (2019)
- [Knowledge-based Biomedical Data Science 2019](https://mlsanity.com/paper/1910.06710/) (2019)
- [Bio-YODIE: A Named Entity Linking System for Biomedical Text](https://mlsanity.com/paper/1811.04860/) (2018)

## Abstract (arXiv)

The healthcare sector is an important pillar of every community, numerous research studies have been carried out in this context to optimize medical processes and improve care quality and facilitate patient management. In this article we have analyzed and examined different works concerning the exploitation of the most recent technologies such as big data, artificial intelligence, machine learning, and deep learning for the improvement of health care, which enabled us to propose our general approach concentrating on the collection, preprocessing and clustering of medical data to facilitate access, after analysis, to the patients and health professionals to predict the most frequent pathologies with better precision within a notable timeframe. keywords: Healthcare, big data, artificial intelligence, automatic language processing, data mining, predictive models.