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Adolescent

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Prediction and unsupervised clustering of fertility intention among migrant workers based on machine learning: a cross-sectional survey from Henan, China.

BMC public health
BACKGROUND: Although China has implemented multiple policies to encourage childbirth, the results have been underwhelming. Migrant workers account for a considerable proportion of China's population, most of whom are of childbearing age. However, few...

Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment: Development of an Automated Machine Learning Model.

Drug design, development and therapy
PURPOSE: Drug-induced liver injury (DILI) is one of the most common and serious adverse drug reactions related to first-line anti-tuberculosis drugs in pediatric tuberculosis patients. This study aims to develop an automatic machine learning (AutoML)...

Using supervised machine learning and ICD10 to identify non-accidental trauma in pediatric trauma patients in the Maryland Health Services Cost Review Commission dataset.

Child abuse & neglect
BACKGROUND: Identifying non-accidental trauma (NAT) in pediatric trauma patients is challenging. We developed a machine learning model that uses demographic characteristics and ICD10 codes to detect the first diagnosis of NAT.

Deep Neural Network Analysis of the 12-Lead Electrocardiogram Distinguishes Patients With Congenital Long QT Syndrome From Patients With Acquired QT Prolongation.

Mayo Clinic proceedings
OBJECTIVE: To test whether an artificial intelligence (AI) deep neural network (DNN)-derived analysis of the 12-lead electrocardiogram (ECG) can distinguish patients with long QT syndrome (LQTS) from those with acquired QT prolongation.

Development of a model for measuring sagittal plane parameters in 10-18-year old adolescents with idiopathic scoliosis based on RTMpose deep learning technology.

Journal of orthopaedic surgery and research
PURPOSE: The study aimed to develop a deep learning model for rapid, automated measurement of full-spine X-rays in adolescents with Adolescent Idiopathic Scoliosis (AIS). A significant challenge in this field is the time-consuming nature of manual me...

Prediction of p-phenylenediamine antioxidant concentrations in human urine using machine learning models.

Journal of hazardous materials
p-phenylenediamine antioxidants (PPDs) are extensively used in rubber manufacturing for their potent antioxidative properties, but PPDs and 2-anilino-5-[(4-methylpentan-2yl)amino]cyclohexa-2,5-diene-1,4-dione (6PPDQ) pose potential environmental and ...

Multi-center brain age prediction via dual-modality fusion convolutional network.

Medical image analysis
Accurate prediction of brain age is crucial for identifying deviations between typical individual brain development trajectories and neuropsychiatric disease progression. Although current research has made progress, the effective application of brain...

Development of deep learning auto-encoder algorithms for predicting alcohol use in Korean adolescents based on cross-sectional data.

Social science & medicine (1982)
Alcohol is a highly addictive substance, presenting significant global public health concerns, particularly among adolescents. Previous studies have been limited by traditional research methods, making it challenging to encompass diverse risk factors...

Machine learning in the prediction of human wellbeing.

Scientific reports
Subjective wellbeing data are increasingly used across the social sciences. Yet, despite the widespread use of such data, the predictive power of approaches commonly used to model wellbeing is only limited. In response, we here use tree-based Machine...

The influence of factors related to public health campaigns on vaccination behavior among population of Wuxi region, China.

Frontiers in public health
BACKGROUND: Public health campaigns are essential for promoting vaccination behavior, but factors such as socioeconomic status, geographical location, campaign quality, and service accessibility influence vaccine uptake. In the Wuxi region of China, ...