AIMC Topic: Adult

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Research on the impact of explosive martial arts training on emotion regulation and attention based on questionnaire data.

Scientific reports
Understanding the psychological effects of martial arts training requires models that can bridge the gap between observable physical behavior and subjective cognitive states. This study proposes a deep learning framework that explicitly uses question...

Multimodal Multitask Learning for Predicting Depression Severity and Suicide Risk Using Pretrained Audio and Text Embeddings: Methodology Development and Application.

JMIR medical informatics
BACKGROUND: Depression is a critical psychological disorder necessitating urgent assessment and treatment, given its strong association with increased suicide risk (SR). Effective management hinges on promptly identifying individuals with high depres...

Evaluation of ChatGPT-5 responses in obstetric and gynecological emergencies: concordance, readability, and clinical reliability.

BMC emergency medicine
BACKGROUND: This study aimed to evaluate the compliance with guidelines, clinical safety, and applicability of ChatGPT-5 responses in obstetric and gynecological emergency scenarios. With the increasing role of AI-powered large language models (LLMs)...

CS-Net: convolutional spider neural network for surface-EMG-based hybrid gesture recognition.

Journal of neural engineering
In this paper, we propose a novel neural network architecture, the convolutional spider neural network (CS-Net), combined with a transfer learning (TL) strategy, to classify hybrid gestures that integrate wrist postures and hand movements.The CS-Net ...

Artificial intelligence (AI)-Enabled behavioral health application for college students: Pilot study protocol.

PloS one
Given the prevalence of depression among young adults, particularly those aged 18-25, this study aims to address a critical need in higher education institutions for proactive, private, automated mental health self-awareness. This study protocol outl...

Predicting hypertension and identifying most important factors among married women in Bangladesh using machine learning approach.

PloS one
INTRODUCTION: Hypertension is a leading contributor to maternal and cardiometabolic morbidity in Bangladesh. We developed and interpreted machine-learning (ML) models to predict hypertension and rank associated factors among married women with the go...

Machine learning-driven risk stratification for distant metastasis in gastric cancer: A comparative study of clinical features and composite indices integrated models.

PloS one
OBJECTIVE: Distant metastasis (DM) of gastric cancer (GC) represents a significant health challenge due to its high mortality rates, necessitating advancements in early detection and management strategies. The objective of this study was to create a ...

Integrative Deep Learning of Genomic and Clinical Data for Predicting Treatment Response in Newly Diagnosed Epilepsy.

Neurology
BACKGROUND AND OBJECTIVES: Epilepsy is a common neurologic disorder. Although antiseizure medications (ASMs) are the first-line treatment, identifying the most effective ASM for each individual remains a trial-and-error process. Genetic variation may...

Non-linear association between Life's Essential 8 and diabetic retinopathy: mediating role of depression in US adults with diabetes.

BMC public health
BACKGROUND: Life's Essential 8 (LE8) is a comprehensive cardiovascular health (CVH) metric that is associated with chronic diseases. This study aimed to investigate the association between LE8 and diabetic retinopathy (DR) and the mediating role of d...

Comparing radiomics, deep learning, and fusion models for predicting occult pleural dissemination in patients with non-small cell lung cancer: a retrospective multicenter study.

BMC cancer
BACKGROUND: Occult pleural dissemination (PD) in non-small cell lung cancer (NSCLC) patients is likely to be missed on computed tomography (CT) scans, associated with poor survival, and generally contraindicated for radical surgery. This study aimed ...