AI-based modular warning machine for risk identification in proximity healthcare
This addresses risk identification in proximity healthcare, but it appears incremental as it applies existing methods to a new dataset.
The study developed an automated pipeline using unsupervised and supervised machine learning methods to interpret multi-modal data for risk identification in proximity healthcare, as part of the DHEAL-COM project.
"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.