Pediatrics

Latest AI and machine learning research in pediatrics for healthcare professionals.

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The Effect of Secondary Traumatic Stress and Cognitive Flexibility on Psychological Well-Being in Health Education Students.

AIM: The aim of this study is to examine the effects of secondary traumatic stress and cognitive fle...

Expert-augmented machine learning for predicting extubation readiness in the pediatric intensive care unit.

BACKGROUND: Determining extubation readiness in pediatric intensive care units (PICU) is challenging...

Development of a machine learning model to identify the predictors of the neonatal intensive care unit admission.

Scientists aim to create a system that can predict the likelihood of newborns being admitted to the ...

Development and evaluation of an automated classification and counting system for rice planthoppers captured on survey boards.

Rice planthoppers are the most economically important insect pests of rice in Asia. Traditional surv...

Machine learning to detect melanoma exploiting nuclei morphology and Spatial organization.

Cutaneous melanoma is one of the most lethal forms of skin cancer, and its incidence is increasing g...

Machine learning-assisted decoding of temporal transcriptional dynamics via fluorescent timer.

Investigating the temporal dynamics of gene expression is crucial for understanding gene regulation ...

Decoding the diagnostic biomarkers of mitochondrial dysfunction related gene variants in pediatric T cell acute lymphoblastic leukemia.

Mitochondrial dysfunction is crucial in the pathogenesis and drug resistance of pediatric T-cell acu...

Integrated artificial intelligence in healthcare and the patient's experience of care.

Healthcare is plagued with many problems that Artificial Intelligence (AI) can ameliorate or sometim...

Closed circuit artificial ıntelligence model named morgaf for childhood onset systemic lupus erythematosus diagnosis.

Systemic Lupus Erythematosus (SLE) is a chronic, autoimmune disease characterized by multiple organ ...

Generative AI for weakly supervised segmentation and downstream classification of brain tumors on MR images.

Segmenting abnormalities is a leading problem in medical imaging. Using machine learning for segment...

Medium-sized protein language models perform well at transfer learning on realistic datasets.

Protein language models (pLMs) can offer deep insights into evolutionary and structural properties o...

Research on the impact of artificial intelligence development on urban low-carbon transformation.

Scientific analysis of the impact of artificial intelligence (AI) development on urban low-carbon tr...

Potential use of saliva infrared spectra and machine learning for a minimally invasive screening test for congenital syphilis in infants.

Congenital syphilis is a global public health issue, and its diagnostic complexity poses a challenge...

Artificial neural network-driven approaches to improved forecasting of disability care expenditures in an aging Kingdom of Saudi Arabia population.

The total number of older persons globally (those aged 60 years and above) was 202 million in 1950; ...

Development of a triangular Fermatean fuzzy EDAS model for remote patient monitoring applications.

Remote Patient Monitoring Systems (RPMS) are vital for tracking patients' health outside clinical se...

RiNALMo: general-purpose RNA language models can generalize well on structure prediction tasks.

While RNA has recently been recognized as an interesting small-molecule drug target, many challenges...

Aggressiveness-guided nodule management for lung cancer screening in Europe-justification for follow-up intervals and definition of growth.

The European Society of Thoracic Imaging (ESTI) nodule management recommendation for lung cancer scr...

Development and validation of an AI-enabled oral score using large-scale dental data.

This research introduces Oral Score Basic (OS-B), a novel Artificial Intelligence (AI) derived metho...

Lessons learned from RadiologyNET foundation models for transfer learning in medical radiology.

Deep learning models require large amounts of annotated data, which are hard to obtain in the medica...

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