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

TL;DR

This paper presents an AI-based modular warning system utilizing machine learning to identify risks in proximity healthcare, aiming to enhance early detection and decision-making processes.

Contribution

It introduces a comprehensive automated pipeline combining unsupervised and supervised methods for interpreting multi-modal healthcare data.

Findings

Effective risk prediction through machine learning algorithms

Enhanced interpretability via feature identification

Potential for improved healthcare decision support

Abstract

"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.