Evidence map›Paper›PMID 39911557›Full record

ArticleFrontiers in psychiatry2024

Proximity-based solutions for optimizing autism spectrum disorder treatment: integrating clinical and process data for personalized care.

Annarita Vignapiano, Francesco Monaco, Stefania Landi, Luca Steardo, Carlo Mancuso, Claudio Pagano, Gianvito Petrillo, Alessandra Marenna, Martina Piacente, Stefano Leo and 8 more

Abstract read
In one paragraph

Article in Frontiers in psychiatry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Annarita Vignapiano *Department of Mental Health, Azienda Sanitaria Locale Salerno, Salerno, Italy.
Francesco Monaco *Department of Mental Health, Azienda Sanitaria Locale Salerno, Salerno, Italy.
Stefania LandiDepartment of Mental Health, Azienda Sanitaria Locale Salerno, Salerno, Italy.
Luca SteardoDepartment of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy.
Carlo MancusoInnovation Technology e Sviluppo (I.T.Svil), Salerno, Italy.
Claudio PaganoInnovation Technology e Sviluppo (I.T.Svil), Salerno, Italy.
Gianvito PetrilloInnovation Technology e Sviluppo (I.T.Svil), Salerno, Italy.
Alessandra MarennaEuropean Biomedical Research Institute of Salerno (EBRIS), Salerno, Italy.
Martina PiacenteEuropean Biomedical Research Institute of Salerno (EBRIS), Salerno, Italy.
Stefano LeoEuropean Biomedical Research Institute of Salerno (EBRIS), Salerno, Italy.
Carminia Marina IngenitoEuropean Biomedical Research Institute of Salerno (EBRIS), Salerno, Italy.
Rossella BonifacioDepartment of Mental Health, Azienda Sanitaria Locale Salerno, Salerno, Italy.
Benedetta Di Gruttola *Department of Mental Health, Azienda Sanitaria Locale Salerno, Salerno, Italy.
Marco SolmiDepartment of Psychiatry, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.
Maria PontilloChildhood and Adolescent Neuropsychiatry Unit, Department of Neuroscience, Bambino Gesù Children's Hospital (IRCCS), Rome, Italy.
Giorgio Di LorenzoDepartment of Systems Medicine, University of Rome Tor Vergata, Rome, Italy.
Alessio FasanoEuropean Biomedical Research Institute of Salerno (EBRIS), Salerno, Italy.
Giulio CorrivettiDepartment of Mental Health, Azienda Sanitaria Locale Salerno, Salerno, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

artificial intelligenceautism spectrum disorderdeep learningmachine learningpatient-centered care

Identifiers

PMID39911557
PMCPMC11795314

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.