DSM-5

Zero-Shot Large Language Models for Preliminary Prediction of PTSD Symptoms From Clinical Interview Transcripts: Grands modèles de langage sans exemple pour la prédiction préliminaire des symptômes de TSPT à partir de transcriptions d'entrevues cliniques.

BackgroundPosttraumatic stress disorder (PTSD) is common yet frequently underdiagnosed, in part due to barriers to systematic screening and the reliance on self-report instruments. Large language models (LLMs) have shown promise in extracting clinically relevant information from unstructured language, but their ability to infer item-level PTSD symptom severity from clinical interviews remains uncl...

Neuroimaging-derived biotypes of self-reported insomnia symptoms are differentially associated with affective symptoms.

BACKGROUND: Sleep problems are among the most prevalent health complaints during adolescence and show a reciprocal relationship with the development of depression and anxiety. Neuroimaging studies have identified heterogeneous neuroanatomical correlates of sleep problems, particularly insomnia symptoms, yet it remains unclear how distinct neural substrates differentially shape the relationships be...

TRUST: an large language model-based dialogue system for trauma understanding and structured assessments.

OBJECTIVES: While large language models (LLMs) have been widely used to assist clinicians and support patients, no existing work has explored dialogue...

A novel approach to soil nutrients prediction model for Bezuidenhout Park, Johannesburg, Gauteng Province, South Africa: attention temporal neural networks (ATNN).

The agricultural sector is moving from the Agriculture 4.0 model to the Agriculture 5.0 model because of advances in machine learning (ML), big data, ...

An advanced machine learning framework for predicting and experimentally validating Nb and Ta effects on the mechanical behavior of high-entropy alloys with reduced experimental dependency.

This study employs machine learning (ML) models such as Decision Tree (DT), Extra Tree (ET), K-Nearest Neighbors (KNN), Support Vector Regressor (SVR)...

Accelerated Brain Aging in Young Women with Posttraumatic Stress Disorder.

Posttraumatic stress disorder (PTSD) has been associated with structural brain alterations, suggesting accelerated brain aging. Evidence from peripher...

AI-Assisted Isolation of Bioactive Dipyrimicins from Amycolatopsis azurea and Identification of Their Corresponding Dip Biosynthetic Gene Cluster.

One of the major challenges in natural product discovery is the prioritization of compounds with useful activities from microbial sources. Here, we ut...

Large Language Model-Powered Diagnostic Co-Pilot ("CapyEngine") for Mental Disorders: Development, Evaluation, and Future Optimization Study.

BACKGROUND: Despite the growing potential of large language models (LLMs) in mental health services, evidence on its capabilities in diagnostic proces...

Assessing the clinical competence of large language models for tobacco use disorder: A multi-domain expert evaluation.

BACKGROUND: Tobacco use disorder (TUD) remains the leading preventable cause of death globally, yet fewer than one-third of users receive guideline-co...

Prediction and Prevalence of Self-Harm and Nonsuicidal Self-Injury in Children With Learning Disabilities: A Machine-Learning Approach in Saudi Arabia.

The current study aimed to estimate the prevalence of self-harm and nonsuicidal self-injury (NSSI) in children with learning disabilities (LD) in Saud...

Development and proof-of-concept of a treatment target recommendation algorithm in the context of cognitive processing therapy.

Background: Cognitive Processing Therapy (CPT) is an effective, widely supported treatment for PTSD, but patient response varies considerably. Optimal...

Identifying past-year self-reported suicidality in outpatients with somatic symptom disorder using an interpretable machine-learning model: a multicenter study with an online calculator.

BACKGROUND: Somatic symptom disorder (SSD) is associated with an elevated risk of suicidality. However, clinically implementable tools to identify out...

Modeling Soil pH at regional scale using environmental covariates and machine learning algorithm.

Soil pH serves as a critical indicator of soil chemistry and fertility, and mapping its spatial distribution holds significant importance for effectiv...

Investigation of cold formed steel angle compression through high throughput design FEA and machine learning.

This research investigates the finite element analysis (FEA) of cold-formed steel (CFS) L-columns with pin-ended supports under compression. The study...

Genetic algorithm-optimized artificial neural network for multi-objective optimization of biomass and exopolysaccharide production by Haloferax mediterranei.

Microbial production of industrially important exopolysaccharide (EPS) from extremophiles has several advantages. In this study, key media components ...

Validation of Machine Learning-Based Assessment of Major Depressive Disorder from Paralinguistic Speech Characteristics in Routine Care.

New developments in machine learning-based analysis of speech can be hypothesized to facilitate the long-term monitoring of major depressive disorder ...

DSM: Deep sequential model for complete neuronal morphology representation and feature extraction.

The full morphology of single neurons is indispensable for understanding cell types, the basic building blocks in brains. Projecting trajectories are ...

Depression in South Korean Adolescents Captured by Text and Opinion Mining of Social Big Data.

Depression in adolescence is recognized as an important social and public health issue that interferes with continued physical growth and increases th...

Sensor-Driven Human-Robot Synergy: A Systems Engineering Approach.

Knowledge-based synergistic automation is a potential intermediate option between the opposite extremes of manual and fully automated robotic labor in...

Survival analysis of localized prostate cancer with deep learning.

In recent years, data-driven, deep-learning-based models have shown great promise in medical risk prediction. By utilizing the large-scale Electronic ...