Psychiatry

Prediction of lithium response using clinical data.

OBJECTIVE: Promptly establishing maintenance therapy could reduce morbidity and mortality in patient...

Machine Learning Interpretation of Extended Human Papillomavirus Genotyping by Onclarity in an Asian Cervical Cancer Screening Population.

This study aimed (i) to compare the performance of the BD Onclarity human papillomavirus (HPV) assay...

Psychiatry and Fitness to Fly After Germanwings.

In March 2015, a co-pilot flying Germanwings Flight 9525 deliberately pointed his airplane into a de...

Use of deep learning to detect personalized spatial-frequency abnormalities in EEGs of children with ADHD.

OBJECTIVE: Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurobehavio...

Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual.

Schizophrenia is a severe psychiatric disorder associated with both structural and functional brain ...

Artificial intelligence and the future of psychiatry: Insights from a global physician survey.

BACKGROUND: Futurists have predicted that new autonomous technologies, embedded with artificial inte...

Implementing machine learning in bipolar diagnosis in China.

Bipolar disorder (BPD) is often confused with major depression, and current diagnostic questionnaire...

Personalized prediction of smartphone-based psychotherapeutic micro-intervention success using machine learning.

BACKGROUND: Tailoring healthcare to patients' individual needs is a central goal of precision medici...

Prospective prediction of suicide attempts in community adolescents and young adults, using regression methods and machine learning.

BACKGROUND: The use of machine learning (ML) algorithms to study suicidality has recently been recom...

A review on neural network models of schizophrenia and autism spectrum disorder.

This survey presents the most relevant neural network models of autism spectrum disorder and schizop...

Predicting rehospitalization within 2 years of initial patient admission for a major depressive episode: a multimodal machine learning approach.

Machine learning methods show promise to translate univariate biomarker findings into clinically use...

Natural language processing of electronic health records is superior to billing codes to identify symptom burden in hemodialysis patients.

Symptoms are common in patients on maintenance hemodialysis but identification is challenging. New i...

Guest editorial: Special issue on machine learning in schizophrenia.

ARPNet: Antidepressant Response Prediction Network for Major Depressive Disorder.

Treating patients with major depressive disorder is challenging because it takes several months for ...

Artificial Intelligence for Mental Health and Mental Illnesses: an Overview.

PURPOSE OF REVIEW: Artificial intelligence (AI) technology holds both great promise to transform men...

Intelligent Sensing to Inform and Learn (InSTIL): A Scalable and Governance-Aware Platform for Universal, Smartphone-Based Digital Phenotyping for Research and Clinical Applications.

In this viewpoint we describe the architecture of, and design rationale for, a new software platform...

A network perspective on body dysmorphic disorder and major depressive disorder.

BACKGROUND: Body dysmorphic disorder (BDD) is a highly debilitating mental disorder associated with ...

Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [S]GTPγS Binding Assays.

G-protein-coupled receptors (GPCRs), also known as 7-transmembrane receptors, are the single largest...

Resting-State Functional Network Scale Effects and Statistical Significance-Based Feature Selection in Machine Learning Classification.

In recent years, functional brain network topological features have been widely used as classificati...

Analysis of risk factor domains in psychosis patient health records.

BACKGROUND: Readmission after discharge from a hospital is disruptive and costly, regardless of the ...

A Research Roadmap: Connected Health as an Enabler of Cancer Patient Support.

The evidence that quality of life is a positive variable for the survival of cancer patients has pro...