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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.15823 |
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| _version_ | 1866912439579181056 |
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| author | Razzetta, Chiara Noei, Shahryar Barbarossa, Federico Spairani, Edoardo Roascio, Monica Barbi, Elisa Ciacci, Giulia Sommariva, Sara Guastavino, Sabrina Piana, Michele Lenge, Matteo Arnulfo, Gabriele Magenes, Giovanni Maranesi, Elvira Amabili, Giulio Massone, Anna Maria Benvenuto, Federico Jurman, Giuseppe Sona, Diego Campi, Cristina |
| author_facet | Razzetta, Chiara Noei, Shahryar Barbarossa, Federico Spairani, Edoardo Roascio, Monica Barbi, Elisa Ciacci, Giulia Sommariva, Sara Guastavino, Sabrina Piana, Michele Lenge, Matteo Arnulfo, Gabriele Magenes, Giovanni Maranesi, Elvira Amabili, Giulio Massone, Anna Maria Benvenuto, Federico Jurman, Giuseppe Sona, Diego Campi, Cristina |
| contents | "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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_15823 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-based modular warning machine for risk identification in proximity healthcare Razzetta, Chiara Noei, Shahryar Barbarossa, Federico Spairani, Edoardo Roascio, Monica Barbi, Elisa Ciacci, Giulia Sommariva, Sara Guastavino, Sabrina Piana, Michele Lenge, Matteo Arnulfo, Gabriele Magenes, Giovanni Maranesi, Elvira Amabili, Giulio Massone, Anna Maria Benvenuto, Federico Jurman, Giuseppe Sona, Diego Campi, Cristina Machine Learning 68T01, 68T05 "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. |
| title | AI-based modular warning machine for risk identification in proximity healthcare |
| topic | Machine Learning 68T01, 68T05 |
| url | https://arxiv.org/abs/2506.15823 |