Prediction of psychotic disorder in individuals with clinical high-risk state by multimodal machine-learning: A preliminary study-Reference-Cited by-同舟云学术

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Prediction of psychotic disorder in individuals with clinical high-risk state by multimodal machine-learning: A preliminary study

Published:2024-06 Issue: Volume:10 Page:100089
ISSN:2666-1446
Container-title:Biomarkers in Neuropsychiatry
language:en
Short-container-title:Biomarkers in Neuropsychiatry

Author:

Takayanagi Yoichiro,Sasabayashi Daiki,Takahashi Tsutomu,Higuchi Yuko,Nishiyama Shimako,Tateno Takahiro,Mizukami Yuko,Akasaki Yukiko,Furuichi Atsushi,Kobayashi Haruko,Takayanagi Mizuho,Noguchi Kyo,Tsujii Noa,Suzuki Michio

Funder

Japan Agency for Medical Research and Development

Japan Society for the Promotion of Science

Publisher

Elsevier BV

Reference20 articles.

1. Prediction of transition from ultra-high risk to first-episode psychosis using a probabilistic model combining history, clinical assessment and fatty-acid biomarkers;Clark;Transl. Psychiatry,2016

2. Disorganized Gyrification Network Properties During the Transition to Psychosis;Das;JAMA Psychiatry,2018

3. FreeSurfer;Fischl;Neuroimage,2012

4. Development and Validation of a Clinically Based Risk Calculator for the Transdiagnostic Prediction of Psychosis;Fusar-Poli;JAMA Psychiatry,2017

5. Electroencephalography and Event-Related Potential Biomarkers in Individuals at Clinical High Risk for Psychosis;Hamilton;Biol. Psychiatry,2020

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