Endocrinology

Menopause

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

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Deep reinforcement learning for decision making of autonomous vehicle in non-lane-based traffic environments.

Existing research on decision-making of autonomous vehicles (AVs) has mainly focused on normal road ...

Prediction of postoperative intensive care unit admission with artificial intelligence models in non-small cell lung carcinoma.

BACKGROUND: There is no standard practice for intensive care admission after non-small cell lung can...

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

Osteoporosis, a bone disease characterized by decreased bone mineral density (BMD) resulting in decr...

UGV-NBWASTE: An oriented dataset for non-biodegradable waste in Bangladesh.

The "UGV-NBWASTE" dataset is built for those who manage non-biodegradable waste. The selection of no...

Deep Learning Approach Readily Differentiates Papilledema, Non-Arteritic Anterior Ischemic Optic Neuropathy, and Healthy Eyes.

OBJECTIVE: Deep learning (DL) has been used in differentiating a range of ophthalmic conditions. We ...

Using the counterfactual framework to estimate non-intention-to-treat estimands in randomised controlled trials: A methodological scoping review.

BACKGROUND: Randomised controlled trials (RCTs) commonly estimate intention-to-treat (ITT) estimands...

The Role of ChatGPT in osteoporosis management: a comparative analysis with clinical expertise.

UNLABELLED: This study evaluates the role of ChatGPT in osteoporosis management, demonstrating 91% d...

An oral robotic pill reliably and safely delivers teriparatide with high bioavailability in healthy volunteers: A phase 1 study.

AIMS: The incidence of osteoporosis is projected to exceed 70 million people over the age of 65 year...

Prioritization strategies for non-target screening in environmental samples by chromatography - High-resolution mass spectrometry: A tutorial.

Non-target screening (NTS) using chromatography coupled to high-resolution mass spectrometry (HRMS),...

Artificial intelligence-based non-invasive bilirubin prediction for neonatal jaundice using 1D convolutional neural network.

Neonatal jaundice, characterized by elevated bilirubin levels causing yellow discoloration of the sk...

Early obesity risk prediction via non-dietary lifestyle factors using machine learning approaches.

Obesity poses a significant health threat, contributing to the development of noncommunicable diseas...

Machine learning models to predict osteoporosis in patients with chronic kidney disease stage 3-5 and end-stage kidney disease.

Chronic kidney disease-mineral bone disorder is a common complication in patients with chronic kidne...

Machine learning-based non-invasive continuous dynamic monitoring of human core temperature with wearable dual temperature sensors.

Due to the growing demand for personal health monitoring in extreme environments, continuous monitor...

Deep Learning-driven Microfluidic-SERS to Characterize the Heterogeneity in Exosomes for Classifying Non-Small Cell Lung Cancer Subtypes.

Lung cancer exhibits strong heterogeneity, and its early diagnosis and precise subtyping are of grea...

Predicting a failure of postoperative thromboprophylaxis in non-small cell lung cancer: A stacking machine learning approach.

BACKGROUND: Non-small-cell lung cancer (NSCLC) and its surgery significantly increase the venous thr...

Leveraging large language models to mimic domain expert labeling in unstructured text-based electronic healthcare records in non-english languages.

BACKGROUND: The integration of big data and artificial intelligence (AI) in healthcare, particularly...

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