Schizophrenia

Fingerprints as Predictors of Schizophrenia: A Deep Learning Study.

BACKGROUND AND HYPOTHESIS: The existing developmental bond between fingerprint generation and growth...

Qualitative and Artificial Intelligence-Based Sentiment Analysis of Turkish Tweets Related to Schizophrenia.

OBJECTIVE: The aim of this study was to qualitatively examine Turkish tweets about schizophrenia in ...

Natural Language Processing and Psychosis: On the Need for Comprehensive Psychometric Evaluation.

BACKGROUND AND HYPOTHESIS: Despite decades of "proof of concept" findings supporting the use of Natu...

Translating Natural Language Processing into Mainstream Schizophrenia Assessment.

Prediction of Early Symptom Remission in Two Independent Samples of First-Episode Psychosis Patients Using Machine Learning.

BACKGROUND: Validated clinical prediction models of short-term remission in psychosis are lacking. O...

Decision support system for the differentiation of schizophrenia and mood disorders using multiple deep learning models on wearable devices data.

In the modern world, with so much inherent stress, mental health disorders (MHDs) are becoming more ...

Implementation of a machine learning algorithm for automated thematic annotations in avatar: A linear support vector classifier approach.

Avatar Therapy (AT) is a modern therapeutic alternative for patients with schizophrenia suffering fr...

De novo generation of dual-target ligands using adversarial training and reinforcement learning.

Artificial intelligence, such as deep generative methods, represents a promising solution to de novo...

Schizophrenia Detection in Adolescents from EEG Signals using Symmetrically weighted Local Binary Patterns.

Schizophrenia is one of the most complex of all mental diseases. In this paper, we propose a symmetr...

Multi-modal deep learning of functional and structural neuroimaging and genomic data to predict mental illness.

Neuropsychiatric disorders such as schizophrenia are very heterogeneous in nature and typically diag...

Obesity as a Risk Factor for Accelerated Brain Ageing in First-Episode Psychosis-A Longitudinal Study.

BACKGROUND: Obesity is highly prevalent in schizophrenia, with implications for psychiatric prognosi...

A 3D multiscale view convolutional neural network with attention for mental disease diagnosis on MRI images.

Computer Assisted Diagnosis (CAD) based on brain Magnetic Resonance Imaging (MRI) is a popular resea...

Robot-induced hallucinations in Parkinson's disease depend on altered sensorimotor processing in fronto-temporal network.

Hallucinations in Parkinson's disease (PD) are disturbing and frequent non-motor symptoms and consti...

Using Natural Language Processing on Electronic Health Records to Enhance Detection and Prediction of Psychosis Risk.

BACKGROUND: Using novel data mining methods such as natural language processing (NLP) on electronic ...

Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression.

IMPORTANCE: Diverse models have been developed to predict psychosis in patients with clinical high-r...

Large-Scale Structural Covariance Networks Predict Age in Middle-to-Late Adulthood: A Novel Brain Aging Biomarker.

The aging process is accompanied by changes in the brain's cortex at many levels. There is growing i...

Computational framework for detection of subtypes of neuropsychiatric disorders based on DTI-derived anatomical connectivity.

Many brain disorders - such as Alzheimer's disease, Parkinson's disease, schizophrenia and autism - ...

MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide.

Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ...

Predicting Early Stage Drug Induced Parkinsonism using Unsupervised and Supervised Machine Learning.

Drug Induced Parkinsonism (DIP) is the most common, debilitating movement disorder induced by antips...

Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning.

Neurobiological heterogeneity in schizophrenia is poorly understood and confounds current analyses. ...

Increasing the Clinical Psychiatric Knowledge Base About Pathogenic Copy Number Variation.

Specific copy number variants (CNVs) have been robustly associated with intellectual disability, aut...