AIMC Topic: China

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Effect of a Cognitive Behavioral Therapy-Based AI Chatbot on Depression and Loneliness in Chinese University Students: Randomized Controlled Trial With Financial Stress Moderation.

JMIR mHealth and uHealth
BACKGROUND: Mental health concerns are prevalent among university students, with financial stress further compounding these issues. While cognitive behavioral therapy (CBT) is effective for these conditions, its delivery through artificial intelligen...

New insights into soil bacteria communities in Beijing urban greenspace based on urbanization gradient.

The Science of the total environment
Research on urban soils has traditionally neglected two significant dimensions: the spatial heterogeneity emerging within megacity resulting from varying urbanization rates, and the dynamic responses of soil microbial communities to ongoing urban exp...

Research on optimal deep learning modeling in HaiNan dialect recognition.

Scientific reports
The speech recognition task of the HaiNan dialect faces significant differences in phonology, intonation, and grammatical structure among dialects, which in turn show significant regionalization characteristics, which makes the task of dialect-to-Man...

Explainable deep learning algorithm for distinguishing IVIG-Resistant Kawasaki disease in Shandong peninsula, China.

BMC pediatrics
BACKGROUND: Intravenous immunoglobulin (IVIG) resistance of Kawasaki disease (KD) patients have a heightened risk of coronary artery lesions. We aimed to explore the predictive factors of IVIG resistance of KD from Shandong Peninsula in China, and es...

Chinese crop diseases and pests named entity recognition based on variational information bottleneck and feature enhancement.

Scientific reports
Chinese crop diseases and pests named entity recognition (CCDP-NER) is a critical step in extracting domain-specific information in the field of crop diseases and pests, playing a significant role in promoting agricultural informatization. To address...

Explainable machine learning identifies key quality-of-life-related predictors of arthritis status: evidence from the China health and retirement longitudinal study.

Health and quality of life outcomes
BACKGROUND: Arthritis is a prevalent chronic disease substantially impacting patients' quality of life (QoL). While identifying key determinants associated with arthritis is critical for targeted interventions, traditional statistical methods often s...

River water quality forecasting: a novel LSTM-Transformer approach enhanced by multi-source data.

Environmental monitoring and assessment
Water quality prediction holds crucial importance as a fundamental technical support for efficient water resource management and strong ecological protection. In this study, aiming to meet the pressing requirement for eutrophication prevention and co...

Development and validation of a machine learning-based survival prediction model for Asian glioblastoma patients using the SEER database and Chinese data.

Scientific reports
Glioblastoma is an aggressive, malignant primary brain tumour and the most prevalent histological type of glioma. Our study attempted to investigate the independent predictors of overall survival (OS) and cancer-specific survival (CSS) in Asian patie...

Research on the synergistic prediction of the suitable distribution and chemical components of Panax Notoginseng under the background of climate warming.

BMC plant biology
Panax notoginseng is a well-known research species in China. The issue of continuous crop barriers has led to a reduction in suitable habitats in Wenshan. In the context of global warming, it is far from adequate to merely predict the suitability dis...

Predictive model integrating deep learning and clinical features based on ultrasound imaging data for surgical intervention in intussusception in children younger than 8 months.

BMJ open
OBJECTIVES: The objective of this study was to identify risk factors for enema reduction failure and to establish a combined model that integrates deep learning (DL) features and clinical features for predicting surgical intervention in intussuscepti...