Proximity-based solutions for optimizing autism spectrum disorder treatment: integrating clinical and process data for personalized care
Autism Spectrum Disorder (ASD) affects millions of individuals worldwide, presenting challenges in social communication, repetitive behaviors, and sensory processing. Despite its prevalence, diagnosis can be lengthy, and access to appropriate treatment varies greatly. This project utilizes the power...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1512818/full |
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| author | Annarita Vignapiano Annarita Vignapiano Francesco Monaco Francesco Monaco Stefania Landi Luca Steardo Carlo Mancuso Claudio Pagano Gianvito Petrillo Alessandra Marenna Martina Piacente Stefano Leo Carminia Marina Ingenito Rossella Bonifacio Benedetta Di Gruttola Marco Solmi Marco Solmi Marco Solmi Marco Solmi Maria Pontillo Giorgio Di Lorenzo Giorgio Di Lorenzo Alessio Fasano Alessio Fasano Alessio Fasano Giulio Corrivetti Giulio Corrivetti |
| author_facet | Annarita Vignapiano Annarita Vignapiano Francesco Monaco Francesco Monaco Stefania Landi Luca Steardo Carlo Mancuso Claudio Pagano Gianvito Petrillo Alessandra Marenna Martina Piacente Stefano Leo Carminia Marina Ingenito Rossella Bonifacio Benedetta Di Gruttola Marco Solmi Marco Solmi Marco Solmi Marco Solmi Maria Pontillo Giorgio Di Lorenzo Giorgio Di Lorenzo Alessio Fasano Alessio Fasano Alessio Fasano Giulio Corrivetti Giulio Corrivetti |
| author_sort | Annarita Vignapiano |
| collection | DOAJ |
| description | Autism Spectrum Disorder (ASD) affects millions of individuals worldwide, presenting challenges in social communication, repetitive behaviors, and sensory processing. Despite its prevalence, diagnosis can be lengthy, and access to appropriate treatment varies greatly. This project utilizes the power of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), to improve Autism Spectrum Disorder diagnosis and treatment. A central data hub, the Master Data Plan (MDP), will aggregate and analyze information from diverse sources, feeding AI algorithms that can identify risk factors for ASD, personalize treatment plans based on individual needs, and even predict potential relapses. Furthermore, the project incorporates a patient-facing chatbot to provide information and support. By integrating patient data, empowering individuals with ASD, and supporting healthcare professionals, this platform aims to transform care accessibility, personalize treatment approaches, and optimize the entire care journey. Rigorous data governance measures will ensure ethical and secure data management. This project will improve access to care, personalize treatments for better outcomes, shorten wait times, boost patient involvement, and raise ASD awareness, leading to better resource allocation. This project marks a transformative shift toward data-driven, patient-centred ASD care in Italy. This platform enhances treatment outcomes for individuals with ASD and provides a scalable model for integrating AI into mental health, establishing a new benchmark for personalized patient care. Through AI integration and collaborative efforts, it aims to redefine mental healthcare standards, enhancing the well-being for individuals with ASD. |
| format | Article |
| id | doaj-art-97224430226e482995d8e5f9ac7391df |
| institution | Kabale University |
| issn | 1664-0640 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj-art-97224430226e482995d8e5f9ac7391df2025-01-22T11:49:27ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402025-01-011510.3389/fpsyt.2024.15128181512818Proximity-based solutions for optimizing autism spectrum disorder treatment: integrating clinical and process data for personalized careAnnarita Vignapiano0Annarita Vignapiano1Francesco Monaco2Francesco Monaco3Stefania Landi4Luca Steardo5Carlo Mancuso6Claudio Pagano7Gianvito Petrillo8Alessandra Marenna9Martina Piacente10Stefano Leo11Carminia Marina Ingenito12Rossella Bonifacio13Benedetta Di Gruttola14Marco Solmi15Marco Solmi16Marco Solmi17Marco Solmi18Maria Pontillo19Giorgio Di Lorenzo20Giorgio Di Lorenzo21Alessio Fasano22Alessio Fasano23Alessio Fasano24Giulio Corrivetti25Giulio Corrivetti26Department of Mental Health, Azienda Sanitaria Locale Salerno, Salerno, ItalyEuropean Biomedical Research Institute of Salerno (EBRIS), Salerno, ItalyDepartment of Mental Health, Azienda Sanitaria Locale Salerno, Salerno, ItalyEuropean Biomedical Research Institute of Salerno (EBRIS), Salerno, ItalyDepartment of Mental Health, Azienda Sanitaria Locale Salerno, Salerno, ItalyDepartment of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro, ItalyInnovation Technology e Sviluppo (I.T.Svil), Salerno, ItalyInnovation Technology e Sviluppo (I.T.Svil), Salerno, ItalyInnovation Technology e Sviluppo (I.T.Svil), Salerno, ItalyEuropean Biomedical Research Institute of Salerno (EBRIS), Salerno, ItalyEuropean Biomedical Research Institute of Salerno (EBRIS), Salerno, ItalyEuropean Biomedical Research Institute of Salerno (EBRIS), Salerno, ItalyEuropean Biomedical Research Institute of Salerno (EBRIS), Salerno, ItalyDepartment of Mental Health, Azienda Sanitaria Locale Salerno, Salerno, ItalyDepartment of Mental Health, Azienda Sanitaria Locale Salerno, Salerno, ItalyDepartment of Psychiatry, Faculty of Medicine, University of Ottawa, Ottawa, ON, CanadaOn Track: The Champlain First Episode Psychosis Program, Department of Mental Health, The Ottawa Hospital, Ottawa, ON, CanadaClinical Epidemiology Program, Ottawa Hospital Research Institute (OHRI), Ottawa, ON, CanadaSchool of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, ON, CanadaChildhood and Adolescent Neuropsychiatry Unit, Department of Neuroscience, Bambino Gesù Children’s Hospital (IRCCS), Rome, Italy0Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy1IRCCS Fondazione Santa Lucia, Rome, ItalyEuropean Biomedical Research Institute of Salerno (EBRIS), Salerno, Italy2Division of Pediatric Gastroenterology and Nutrition, Department of Pediatrics, Massachusetts General Hospital for Children, Harvard Medical School, Boston, MA, United States3Mucosal Immunology Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United StatesDepartment of Mental Health, Azienda Sanitaria Locale Salerno, Salerno, ItalyEuropean Biomedical Research Institute of Salerno (EBRIS), Salerno, ItalyAutism Spectrum Disorder (ASD) affects millions of individuals worldwide, presenting challenges in social communication, repetitive behaviors, and sensory processing. Despite its prevalence, diagnosis can be lengthy, and access to appropriate treatment varies greatly. This project utilizes the power of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), to improve Autism Spectrum Disorder diagnosis and treatment. A central data hub, the Master Data Plan (MDP), will aggregate and analyze information from diverse sources, feeding AI algorithms that can identify risk factors for ASD, personalize treatment plans based on individual needs, and even predict potential relapses. Furthermore, the project incorporates a patient-facing chatbot to provide information and support. By integrating patient data, empowering individuals with ASD, and supporting healthcare professionals, this platform aims to transform care accessibility, personalize treatment approaches, and optimize the entire care journey. Rigorous data governance measures will ensure ethical and secure data management. This project will improve access to care, personalize treatments for better outcomes, shorten wait times, boost patient involvement, and raise ASD awareness, leading to better resource allocation. This project marks a transformative shift toward data-driven, patient-centred ASD care in Italy. This platform enhances treatment outcomes for individuals with ASD and provides a scalable model for integrating AI into mental health, establishing a new benchmark for personalized patient care. Through AI integration and collaborative efforts, it aims to redefine mental healthcare standards, enhancing the well-being for individuals with ASD.https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1512818/fullautism spectrum disorderartificial intelligencemachine learningdeep learningpatient-centered care |
| spellingShingle | Annarita Vignapiano Annarita Vignapiano Francesco Monaco Francesco Monaco Stefania Landi Luca Steardo Carlo Mancuso Claudio Pagano Gianvito Petrillo Alessandra Marenna Martina Piacente Stefano Leo Carminia Marina Ingenito Rossella Bonifacio Benedetta Di Gruttola Marco Solmi Marco Solmi Marco Solmi Marco Solmi Maria Pontillo Giorgio Di Lorenzo Giorgio Di Lorenzo Alessio Fasano Alessio Fasano Alessio Fasano Giulio Corrivetti Giulio Corrivetti Proximity-based solutions for optimizing autism spectrum disorder treatment: integrating clinical and process data for personalized care Frontiers in Psychiatry autism spectrum disorder artificial intelligence machine learning deep learning patient-centered care |
| title | Proximity-based solutions for optimizing autism spectrum disorder treatment: integrating clinical and process data for personalized care |
| title_full | Proximity-based solutions for optimizing autism spectrum disorder treatment: integrating clinical and process data for personalized care |
| title_fullStr | Proximity-based solutions for optimizing autism spectrum disorder treatment: integrating clinical and process data for personalized care |
| title_full_unstemmed | Proximity-based solutions for optimizing autism spectrum disorder treatment: integrating clinical and process data for personalized care |
| title_short | Proximity-based solutions for optimizing autism spectrum disorder treatment: integrating clinical and process data for personalized care |
| title_sort | proximity based solutions for optimizing autism spectrum disorder treatment integrating clinical and process data for personalized care |
| topic | autism spectrum disorder artificial intelligence machine learning deep learning patient-centered care |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1512818/full |
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