Psychiatry

iMEGES: integrated mental-disorder GEnome score by deep neural network for prioritizing the susceptibility genes for mental disorders in personal genomes.

BACKGROUND: A range of rare and common genetic variants have been discovered to be potentially associated with mental diseases, but many more have not been uncovered. Powerful integrative methods are needed to systematically prioritize both variants and genes that confer susceptibility to mental diseases in personal genomes of individual patients and to facilitate the development of personalized t...

BP neural network prediction model for suicide attempt among Chinese rural residents.

OBJECTIVE: This study aimed to establish and assess the Back Propagation Neural Network (BPNN) prediction model for suicide attempt, so as to improve the individual prediction accuracy.

Predicting persistent depressive symptoms in older adults: A machine learning approach to personalised mental healthcare.

BACKGROUND: Depression causes significant physical and psychosocial morbidity. Predicting persistence of depressive symptoms could permit targeted pre...

Resting-state anticorrelated networks in Schizophrenia.

Converging evidences from different lines of research suggest abnormalities in functional brain connectivity in schizophrenia. While positively correl...

Group Differences in Facial Emotion Expression in Autism: Evidence for the Utility of Machine Classification.

Effective social communication relies, in part, on accurate nonverbal expression of emotion. To evaluate the nature of facial emotion expression (FEE)...

Computer vision and behavioral phenotyping: an autism case study.

Despite significant recent advances in molecular genetics and neuroscience, behavioral ratings based on clinical observations are still the gold stand...

A Machine Learning Approach to Reveal the NeuroPhenotypes of Autisms.

Although much research has been undertaken, the spatial patterns, developmental course, and sexual dimorphism of brain structure associated with autis...

A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder.

Mobile technologies offer new opportunities for prospective, high resolution monitoring of long-term health conditions. The opportunities seem of part...

Predicting instructed simulation and dissimulation when screening for depressive symptoms.

The intentional distortion of test results presents a fundamental problem to self-report-based psychiatric assessment, such as screening for depressiv...

Machine learning for predicting psychotic relapse at 2 years in schizophrenia in the national FACE-SZ cohort.

BACKGROUND: Predicting psychotic relapse is one of the major challenges in the daily care of schizophrenia.

Use of natural language processing in electronic medical records to identify pregnant women with suicidal behavior: towards a solution to the complex classification problem.

We developed algorithms to identify pregnant women with suicidal behavior using information extracted from clinical notes by natural language processi...

Identifying disease genes using machine learning and gene functional similarities, assessed through Gene Ontology.

Identifying disease genes from a vast amount of genetic data is one of the most challenging tasks in the post-genomic era. Also, complex diseases pres...

From cognitive and clinical substrates to functional profiles: Disentangling heterogeneity in schizophrenia.

The relationship between neurocognition and functioning among patients with schizophrenia is well documented. However, integrating neuropsychological,...

Ascertaining Depression Severity by Extracting Patient Health Questionnaire-9 (PHQ-9) Scores from Clinical Notes.

The Patient Health Questionnaire-9 (PHQ-9) is a validated instrument for assessing depression severity. While some electronic health record (EHR) syst...

Optimizing Corpus Creation for Training Word Embedding in Low Resource Domains: A Case Study in Autism Spectrum Disorder (ASD).

Automating the extraction of behavioral criteria indicative of Autism Spectrum Disorder (ASD) in electronic health records (EHRs) can contribute signi...

Disease comorbidity-guided drug repositioning: a case study in schizophrenia.

UNLABELLED: The key to any computational drug repositioning is the availability of relevant data in machine-understandable format. While large amount ...

Self-limited single nanowire systems combining all-in-one memristive and neuromorphic functionalities.

The ability for artificially reproducing human brain type signals' processing is one of the main challenges in modern information technology, being on...

Learning Spatial-Spectral-Temporal EEG Features With Recurrent 3D Convolutional Neural Networks for Cross-Task Mental Workload Assessment.

Mental workload assessment is essential for maintaining human health and preventing accidents. Most research on this issue is limited to a single task...

High-throughput brain activity mapping and machine learning as a foundation for systems neuropharmacology.

Technologies for mapping the spatial and temporal patterns of neural activity have advanced our understanding of brain function in both health and dis...

Toward a motor signature in autism: Studies from human-machine interaction.

BACKGROUND: Autism spectrum disorder (ASD) is a heterogeneous group of neurodevelopmental disorders which core symptoms are impairments in socio-commu...