AIMC Topic: China

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Death risk prediction model for patients with non-traumatic intracerebral hemorrhage.

BMC medical informatics and decision making
BACKGROUND: This study aimed to assess the risk of death from non-traumatic intracerebral hemorrhage (ICH) using a machine learning model.

Prediction Trough Concentrations of Valproic Acid Among Chinese Adult Patients with Epilepsy Using Machine Learning Techniques.

Pharmaceutical research
OBJECTIVE: This study aimed to establish an optimal model based on machine learning (ML) to predict Valproic acid (VPA) trough concentrations in Chinese adult epilepsy patients.

Using machine learning models to predict the dose-effect curve of municipal wastewater for zebrafish embryo toxicity.

Journal of hazardous materials
Municipal wastewater substantially contributes to aquatic ecological risks. Assessing the toxicity of municipal wastewater through dose-effect curves is challenging owing to the time-consuming, labor-intensive, and costly nature of biological assays....

Interpretable Machine Learning Model for Predicting Postpartum Depression: Retrospective Study.

JMIR medical informatics
BACKGROUND: Postpartum depression (PPD) is a prevalent mental health issue with significant impacts on mothers and families. Exploring reliable predictors is crucial for the early and accurate prediction of PPD, which remains challenging.

Artificial intelligence and employee outcomes: Investigating the role of job insecurity and technostress in the hospitality industry.

Acta psychologica
Drawing on self-determination theory (SDT), this research examines how the utilization of artificial intelligence (AI) in hospitality organizations is influencing employee work and career outcomes (well-being and career success). We explore the under...

Exploring the impact of natural and human activities on vegetation changes: An integrated analysis framework based on trend analysis and machine learning.

Journal of environmental management
Climate, human activities and terrain are crucial factors influencing vegetation changes. Despite their crucial role, there is a notable lack of research exploring the nonlinear relationships between them and vegetation changes, especially over exten...

Ecological risks of PFAS in China's surface water: A machine learning approach.

Environment international
The persistence of per- and polyfluoroalkyl substances (PFAS) in surface water can pose risks to ecosystems, while due to data limitations, the occurrence, risks, and future trends of PFAS at large scales remain unknown. This study investigated the e...

Informing Risk Hotspots and Critical Mitigations for Rainstorms Using Machine Learning: Evidence from 268 Chinese Cities.

Environmental science & technology
Climate change is exacerbating rainstorms, increasing the risk of flooding and threatening urban sustainability, which could undermine climate action. Here, we propose a machine learning-based framework to assess heterogeneous risks and identify crit...

Leveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approach.

Scientific reports
A dynamics informed neural networks (DINNs) incorporating the susceptible-exposed-infectious-recovered-vaccinated (SEIRV) model was developed to enhance the understanding of the temporal evolution dynamics of infectious diseases. This work integrates...

Development of a Deep Learning-Based Model for Pressure Injury Surface Assessment.

Journal of clinical nursing
AIM: To develop a deep learning-based smart assessment model for pressure injury surface.