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Utilizing machine learning and geographic analysis to improve Post-crash traffic injury management and emergency response systems.

International journal of injury control and safety promotion
Traffic injuries are a major public health concern globally. This study uses machine learning (ML) and geographic analysis to analyse road traffic fatalities and improve traffic safety in Nakhon Ratchasima Province, Thailand. Data on road traffic fat...

A prediction model of pediatric bone density from plain spine radiographs using deep learning.

Scientific reports
Osteoporosis, a bone disease characterized by decreased bone mineral density (BMD) resulting in decreased mechanical strength and an increased fracture risk, remains poorly understood in children. Herein, we developed/validated a deep learning-based ...

A deep learning approach for blood glucose monitoring and hypoglycemia prediction in glycogen storage disease.

Scientific reports
Glycogen storage disease (GSD) is a group of rare inherited metabolic disorders characterized by abnormal glycogen storage and breakdown. These disorders are caused by mutations in G6PC1, which is essential for proper glucose storage and metabolism. ...

Machine learning models for improving the diagnosing efficiency of skeletal class I and III in German orthodontic patients.

Scientific reports
The precise and efficient diagnosis of an individual's skeletal class is necessary in orthodontics to ensure correct and stable treatment planning. However, it is difficult to efficiently determine the true skeletal class due to several correlations ...

Artificial intelligence-supported occupational therapy program on handwriting skills in children at risk for developmental coordination disorder: Randomized controlled trial.

Research in developmental disabilities
AIM: This study investigates the impact of an AI-supported occupational therapy program, developed using the Model of Human Occupation (MOHO), on handwriting skills in children at risk for Developmental Coordination Disorder (DCD).

Epidemiology characteristics and clinical outcomes of composite Hodgkin lymphoma and diffuse large B-cell lymphoma using machine learning.

The oncologist
Composite lymphoma (CL) is rare. We conducted an analysis of 53 329 cases of diffuse large B-cell lymphoma (DLBCL), 17,916 cases of Hodgkin lymphoma (HL), and 869 cases of composite HL and DLBCL from the SEER database diagnosed between 2000 and 2019....

Explainable AI for enhanced accuracy in malaria diagnosis using ensemble machine learning models.

BMC medical informatics and decision making
BACKGROUND: Malaria, an infectious disease caused by protozoan parasites belonging to the Plasmodium genus, remains a significant public health challenge, with African regions bearing the heaviest burden. Machine learning techniques have shown great ...

Exploring cortical excitability in children with cerebral palsy through lower limb robot training based on MI-BCI.

Scientific reports
This study aims to compare brain activity differences under the motor imagery-brain-computer interface (MI-BCI), motor imagery (MI), and resting (REST) paradigms through EEG microstate and functional connectivity (FC) analysis, providing a theoretica...

Prediction of outpatient visits for allergic rhinitis using an artificial intelligence LSTM model - a study in Eastern China.

BMC public health
BACKGROUND: Allergic rhinitis is a common disease that can affect the health of patients and bring huge social and economic burdens. In this study, we developed a model to predict the incidence rate of allergic rhinitis so as to provide accurate info...