Psychiatry

Unmet Needs in Acute Hepatic Porphyria Diagnosis: A Comparative Big Data Analysis of an AI-based Human-in-the-Loop Screening Versus Standard of Care

Acute Hepatic Porphyria (AHP) is a rare genetic disease characterized by unpredictable life-threatening attacks. There is no reliable biochemical screening test for patients outside of an attack and diagnosis is delayed on average by 15 (!) years. AI screening systems can assist in detecting AHP patients, but validating such systems is challenging, due to the limited number of suspected candidates...

Towards Understanding Bipolar Disorder Through Social Media and Transformer Models: Challenges and Insights

Social media presents a promising avenue for monitoring mental health, yet detecting bipolar disorder (BD) remains significantly underexplored. The complexity arises from the overlap of linguistic patterns associated with depression and anxiety, making accurate identification challenging. This study aims to benchmark the performance of various transformer models trained on Reddit posts, to disting...

Predicting agranulocytosis in patients treated with clozapine – development and validation of a machine learning algorithm based on 5,550 patients

To prevent clozapine-induced agranulocytosis (CIA), patients’ white blood cell counts are closely monitored, with treatment stopped if the absolute ne...

Do Large Language Models Have a Personality? A Psychometric Evaluation with Implications for Clinical Medicine and Mental Health AI

Large language models (LLMs) are increasingly used in clinical medicine to provide emotional support, deliver cognitive-behavioral therapy, and assist...

Leveraging Feature Transfer to Predict Medication Resistance and Secondary-Clinical Outcomes in Psychotic Disorders in Forensic Settings

Medication resistance in psychotic disorders represents a critical challenge in forensic psychiatry, where up to 50% of patients show poor treatment r...

Evaluating Enhanced LLMs for Precise Mental Health Diagnosis from Clinical Notes

Anxiety, depression, and other mental health conditions are affecting millions of people worldwide each year. However, limited access to mental health...

Leveraging Deep Learning to Enhance MRI for Brain Disorders

The limited availability and high cost of 7 Tesla (7T) structural MRI hinder its widespread application despite its superior imaging quality. This stu...

Neuroimaging-AI endophenotypes reveal underlying mechanisms and genetic factors contributing to progression and development of four brain disorders

Recent work leveraging artificial intelligence has offered promise to dissect disease heterogeneity by identifying complex intermediate brain phenotyp...

Computational Strategies for Depression Detection and Treatment: The Role of Behavioral Activation and Neurobiological Insights – A Systematic Review

Depression is a multifaceted disorder with neurobiological, behavioral, and environmental components. This review aims to explore how artificial intel...

Development and validation of a personalised antipsychotic selection tool for first-line treatment in severe mental illness

Guidance is lacking on choice of first-line antipsychotic for individuals with incident severe mental illness (SMI). Patients may try several before a...

SleepDepNet: A Multi-Task Transformer Framework for Assessing Sleep Quality and Depression Risk from Social Media Narratives

The bidirectional relationship between sleep disturbances and depression presents a serious challenge for digital mental health research and intervent...

A Computational Ethology Approach for Characterizing Behavioral Dynamics in Bipolar Disorder

Recent technologies for quantifying behavior have revolutionized animal studies in social, cognitive, and pharmacological neurosciences. However, comp...

Redefining Autism Subtypes: a machine learning approach leveraging topological data analysis, network measures and hemispheric lateralization

Autism subtypes, including general Autism Spectrum Disorder (ASD) and Asperger Syndrome (AS), exhibit distinct neural connectivity patterns. This stud...

Predicting and Preventing Suicide at Entry to Mental Health Care: A Community-Engaged, Machine Learning Model Implementation

Suicide rates in the United States have increased steadily over the past twenty years, a trend coinciding with rising use of mental health services ac...

Deep Learning Cerebellar Magnetic Resonance Imaging Segmentation in Late-Onset GM2 Gangliosidosis: Implications for Phenotype

Late-onset Tay-Sachs (LOTS) disease and late-onset Sandhoff disease (LOSD) have long been considered indistinguishable due to similar clinical present...

The dark side of the mood: structural and functional fronto-insular and cerebellar alterations classify major depression

Despite major depressive disorder (MDD) being the leading cause of disability worldwide, the exact characterization of its neural bases and the develo...

Early Detection of Autism Spectrum Disorder in Children Using Different Machine Learning Algorithms

Autism spectrum disorder(ASD) is a neurological condition marked by impaired communication abilities, social detachment, and repetitive behaviors in i...

Cross-Disorder Machine Learning Uncovers Schizophrenia Risk Variants Predictive of Alzheimer’s Disease

Alzheimer’s disease (AD) and Schizophrenia (SCZ) exhibit overlapping clinical features and biological mechanisms, but the extent of their shared genet...

Functional improvement is a better predictor of steady work than medical improvement for individuals with mental health conditions

The Supported Employment Demonstration (SED) offered vocational and mental health services to recently denied disability benefit applicants with menta...

Silencer variants are key drivers of gene upregulation in Alzheimer’s disease

Alzheimer’s disease (AD), particularly late-onset AD, stands as the most prevalent neurodegenerative disorder globally. Owing to its substantial herit...