AIMC Topic: Psychotic Disorders
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False Responses From Artificial Intelligence Models Are Not Hallucinations.
Schizophrenia bulletin
Sep 7, 2023
A Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging Data.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2023
Understanding the structural and functional mechanisms of the brain is challenging for mood and mental disorders. Many neuroimaging techniques are widely used to reveal hidden patterns from different brain imaging modalities. However, these findings ...
Fingerprints as Predictors of Schizophrenia: A Deep Learning Study.
Schizophrenia bulletin
May 3, 2023
BACKGROUND AND HYPOTHESIS: The existing developmental bond between fingerprint generation and growth of the central nervous system points to a potential use of fingerprints as risk markers in schizophrenia. However, the high complexity of fingerprint...
Natural Language Processing and Psychosis: On the Need for Comprehensive Psychometric Evaluation.
Schizophrenia bulletin
Sep 1, 2022
BACKGROUND AND HYPOTHESIS: Despite decades of "proof of concept" findings supporting the use of Natural Language Processing (NLP) in psychosis research, clinical implementation has been slow. One obstacle reflects the lack of comprehensive psychometr...
Machine Learning Identifies Digital Phenotyping Measures Most Relevant to Negative Symptoms in Psychotic Disorders: Implications for Clinical Trials.
Schizophrenia bulletin
Mar 1, 2022
BACKGROUND: Digital phenotyping has been proposed as a novel assessment tool for clinical trials targeting negative symptoms in psychotic disorders (PDs). However, it is unclear which digital phenotyping measurements are most appropriate for this pur...
Prediction of Early Symptom Remission in Two Independent Samples of First-Episode Psychosis Patients Using Machine Learning.
Schizophrenia bulletin
Jan 21, 2022
BACKGROUND: Validated clinical prediction models of short-term remission in psychosis are lacking. Our aim was to develop a clinical prediction model aimed at predicting 4-6-week remission following a first episode of psychosis.
Obesity as a Risk Factor for Accelerated Brain Ageing in First-Episode Psychosis-A Longitudinal Study.
Schizophrenia bulletin
Oct 21, 2021
BACKGROUND: Obesity is highly prevalent in schizophrenia, with implications for psychiatric prognosis, possibly through links between obesity and brain structure. In this longitudinal study in first episode of psychosis (FEP), we used machine learnin...
Robot-induced hallucinations in Parkinson's disease depend on altered sensorimotor processing in fronto-temporal network.
Science translational medicine
Apr 28, 2021
Hallucinations in Parkinson's disease (PD) are disturbing and frequent non-motor symptoms and constitute a major risk factor for psychosis and dementia. We report a robotics-based approach applying conflicting sensorimotor stimulation, enabling the i...
Using Natural Language Processing on Electronic Health Records to Enhance Detection and Prediction of Psychosis Risk.
Schizophrenia bulletin
Mar 16, 2021
BACKGROUND: Using novel data mining methods such as natural language processing (NLP) on electronic health records (EHRs) for screening and detecting individuals at risk for psychosis.
Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression.
JAMA psychiatry
Feb 1, 2021
IMPORTANCE: Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to yo...
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