AIMC Topic: China

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Urban and rural disparities in stroke prediction using machine learning among Chinese older adults.

Scientific reports
Stroke is a significant health concern in China. Differences in stroke risk between rural and urban areas have been highlighted in prior research. However, there is a scarcity of studies on urban-rural differences in predicting stroke. This study aim...

Assessing the effectiveness of long short-term memory and artificial neural network in predicting daily ozone concentrations in Liaocheng City.

Scientific reports
Ozone pollution affects food production, human health, and the lives of individuals. Due to rapid industrialization and urbanization, Liaocheng has experienced increasing of ozone concentration over several years. Therefore, ozone has become a major ...

Machine learning for early diagnosis of Kawasaki disease in acute febrile children: retrospective cross-sectional study in China.

Scientific reports
Early diagnosis of Kawasaki disease (KD) allows timely treatment to be initiated, thereby preventing coronary artery aneurysms in children. However, it is challenging due to the subjective nature of the diagnostic criteria. This study aims to develop...

Chinese medical named entity recognition utilizing entity association and gate context awareness.

PloS one
Recognizing medical named entities is a crucial aspect of applying deep learning in the medical domain. Automated methods for identifying specific entities from medical literature or other texts can enhance the efficiency and accuracy of information ...

CECRel: A joint entity and relation extraction model for Chinese electronic medical records of coronary angiography via contrastive learning.

Journal of biomedical informatics
Entity and relation extraction from Chinese electronic medical records (EMRs) is a crucial foundation for constructing medical knowledge graphs and supporting downstream tasks. Chinese EMRs face challenges in accurately extracting medical entity rela...

Machine learning based on nutritional assessment to predict adverse events in older inpatients with possible sarcopenia.

Aging clinical and experimental research
BACKGROUND: The accuracy of current tools for predicting adverse events in older inpatients with possible sarcopenia is still insufficient to develop individualized nutrition-related management strategies. The objectives were to develop a machine lea...

Uncovering soil heavy metal pollution hotspots and influencing mechanisms through machine learning and spatial analysis.

Environmental pollution (Barking, Essex : 1987)
Soil heavy metal (HM) pollution is a significant and widespread environmental issue in China, highlighting the need to quantify influencing factors and identify priority concern areas for effective prevention and management. Based on published litera...

Predicting sleep quality among college students during COVID-19 lockdown using a LASSO-based neural network model.

BMC public health
BACKGROUND: In March 2022, a new outbreak of COVID-19 emerged in Quanzhou, leading to the implementation of strict lockdown management measures in colleges. While existing research has indicated that the pandemic has had a significant impact on sleep...

Automated segmentation by SCA-UNet can be directly used for radiomics diagnosis of thymic epithelial tumors.

European journal of radiology
BACKGROUND: Automatic segmentation of thymic lesions in preoperative computed tomography (CT) images is crucial for accurate diagnosis but remains time-consuming. Although UNet is widely used in medical imaging, its performance is limited by the inhe...