# AI-based modular warning machine for risk identification in proximity healthcare

arXiv: https://arxiv.org/abs/2506.15823 · HTML: https://mlsanity.com/paper/2506.15823/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 42 references that are in the map.
- First posted on arXiv 13 Jun 2025.

## Builds on (most cited first, 7 of 7)

- [The many Shapley values for model explanation](https://mlsanity.com/paper/1908.08474/) (2019)
- [Tunability: Importance of Hyperparameters of Machine Learning Algorithms](https://mlsanity.com/paper/1802.09596/) (2018)
- [A Comparative Analysis of XGBoost](https://mlsanity.com/paper/1911.01914/) (2019)
- [Elastic Net Regularization Paths for All Generalized Linear Models](https://mlsanity.com/paper/2103.03475/) (2021)
- [Global Convergence and Stability of Stochastic Gradient Descent](https://mlsanity.com/paper/2110.01663/) (2021)
- [Revisiting Agglomerative Clustering](https://mlsanity.com/paper/2005.07995/) (2020)
- [Three-dimensional numerical schemes for the segmentation of the psoas muscle in X-ray computed tomography images](https://mlsanity.com/paper/2312.05887/) (2023)

## Abstract (arXiv)

"DHEAL-COM - Digital Health Solutions in Community Medicine" is a research and technology project funded by the Italian Department of Health for the development of digital solutions of interest in proximity healthcare. The activity within the DHEAL-COM framework allows scientists to gather a notable amount of multi-modal data whose interpretation can be performed by means of machine learning algorithms. The present study illustrates a general automated pipeline made of numerous unsupervised and supervised methods that can ingest such data, provide predictive results, and facilitate model interpretations via feature identification.