COOKIES USE
We use necessary system cookies for the correct functioning of the website and optional Google Analytics cookies to obtain visit statistics.
 +info

Cookies config

  • Necessary

    The necessary cookies are absolutely essential for the website to work properly. This category only includes cookies that guarantee basic website security and functionality. These cookies do not store any personal information.

    NameProveedorPropiedadFinalidadCaducidad+info
    _GRECAPTCHAgoogle.comOwnprovide antispam protection with the reCaptcha service6 months
    cc_cookie_acceptfidmag.orgOwnUsada per confirmar que l'usuari ha confirmat / refusat les cookies (i quins tipus accepta)1 any
    WEB_SESSIONfidmag.orgOwnCookie técnica: cookie de sessió PHP. Guarda l'id de sessió d'usuari.al acabar la sessió

  • Analisys

    Analytical cookies are used to understand how visitors interact with the website. These cookies help to provide information on meters, the name of visitors, the percentage of bounces, the font of the traffic, etc.

    NameProveedorPropiedadFinalidadCaducidad+info
    _gaGoogle AnalyticsFrom third partiesCookie d'anàlisi o mesurament: Identifica els usuaris i proporciona informació sobre com els usuaris troben la pàgina web i com la utilitzen per a realització d'Informes estadístics2 anys
    _gat_gtag_UA_141706552_1Google AnalyticsFrom third partiesCookie d'anàlisi o mesurament: Tracking per part de google per google analytics1 minut
    _gidGoogle AnalyticsFrom third partiesCookie d'anàlisi o mesurament: S'usa per limitar el percentatge de sol·licituds24 hores

ConfigureReject allAccept

Title

Prediction of Evolution in First Episode Psychosis by Means of Multi-Input Deep Learning Algorithms

Summary

Objectives: Deep learning (DL) algorithms lay at the core of most successful artificial  intelligence applications developed in recent years. In this project we will take advantage of their capacity to easily and smoothly combine data of very different nature and to exploit the predictive power of previously fitted DL models on similar data to make predictions on the evolution of individuals with a first episode of psychosis (FEP). Specifically, we will generate DL algorithms combining data of different types (see next paragraph) sampled at the onset of a first episode with previously fitted DL models on schizophrenia and bipolar disorder patients with the aim of predicting diagnosis, levels of clinical severity and degree of functionality at six months and at one year of the onset.    
 
Methodology: Functional, structural and diffusion brain MRI images, fingerprint images, genetic data on single nucleotide polymorphisms (SNPs) previously related to schizophrenia and bipolar disorder, and scores from clinical, functionality and cognition scales will be obtained from a sample of N = 200 FEP patients. This data will be combined with a large database of historical data of similar nature previously sampled at our institutions in order to generate DL predictive algorithms of FEP evolution.

Financing entity

Instituto de Salud Carlos III, con la cofinanciación de Fondo Europeo de Desarrollo Regional "Una manera de hacer Europa"

Amount

75.020,00 €

 

We are part of
HH Província España
Contact us

Avda. Jordà, 8, 08035 Barcelona
Contact phone: 935 480 105
E-mail: fundacio@fidmag.org
Online contact 

           

 

Reconocimientos a la calidad y la excelencia
Última modificación: 29/02/2024