AIMC Topic: China

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Identifying diagnosis evidence of cardiogenic stroke from Chinese echocardiograph reports.

BMC medical informatics and decision making
BACKGROUND: Cardiogenic stroke has increasing morbidity in China and brought economic burden to patient families. In cardiogenic stroke diagnosis, echocardiograph examination is one of the most important examinations. Sonographers will investigate pa...

Heavy metals in submicronic particulate matter (PM) from a Chinese metropolitan city predicted by machine learning models.

Chemosphere
The aim of this study was to establish a method for predicting heavy metal concentrations in PM (aerosol particles with an aerodynamic diameter ≤ 1.0 μm) based on back propagation artificial neural network (BP-ANN) and support vector machine (SVM) me...

Metatranscriptomics reveals the gene functions and metabolic properties of the major microbial community during Chinese Sichuan Paocai fermentation.

Food microbiology
Chinese Sichuan Paocai (CSP) is one of the world's best-known fermented vegetables with a large presence in the Chinese market. The dynamic microbial community is the main contributor to Paocai fermentation. However, little is known about the ecologi...

COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm.

Frontiers in public health
Integration of artificial intelligence (AI) techniques in wireless infrastructure, real-time collection, and processing of end-user devices is now in high demand. It is now superlative to use AI to detect and predict pandemics of a colossal nature. T...

A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images.

European radiology
OBJECTIVES: To utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents.

A novel optimized repeatedly random undersampling for selecting negative samples: A case study in an SVM-based forest fire susceptibility assessment.

Journal of environmental management
The negative sample selection method is a key issue in studies of using machine learning approaches to spatially assess natural hazards. Recently, a Repeatedly Random Undersampling (RRU) was proposed to address the randomness problem faced in Single ...

Development of a Deep Learning Model to Identify Lymph Node Metastasis on Magnetic Resonance Imaging in Patients With Cervical Cancer.

JAMA network open
IMPORTANCE: Accurate identification of lymph node metastasis preoperatively and noninvasively in patients with cervical cancer can avoid unnecessary surgical intervention and benefit treatment planning.

Multitemporal time series analysis using machine learning models for ground deformation in the Erhai region, China.

Environmental monitoring and assessment
Ground deformation (GD) has been widely reported as a global issue and is now an ongoing problem that will profoundly endanger the public safety. GD is a complex and dynamic problem with many contributing factors that occur over time. In the literatu...

Amur tiger stripes: individual identification based on deep convolutional neural network.

Integrative zoology
The automatic individual identification of Amur tigers (Panthera tigris altaica) is important for population monitoring and making effective conservation strategies. Most existing research primarily relies on manual identification, which does not sca...

Lower Adenoma Miss Rate of Computer-Aided Detection-Assisted Colonoscopy vs Routine White-Light Colonoscopy in a Prospective Tandem Study.

Gastroenterology
BACKGROUND AND AIMS: Up to 30% of adenomas might be missed during screening colonoscopy-these could be polyps that appear on-screen but are not recognized by endoscopists or polyps that are in locations that do not appear on the screen at all. Comput...