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Uncovering age-specific subtypes of pediatric obesity and metabolic syndrome using machine learning algorithms.

Scientific reports
Identifying new subgroups among children and adolescents with obesity and metabolic syndrome requires advanced clustering techniques capable of analyzing complex multidimensional data. This study aimed to employ machine learning methods to enhance th...

Multimodal fusion of ultrasound images using HXM net for breast cancer diagnosis.

Scientific reports
Breast cancer is a major global health issue in women, where diagnosis at an early stage is decisive for enhancing the effectiveness of treatment and survival. Despite the advances in imaging using medical technologies, maintaining uniformly good dia...

Hyperplastic growth, not hydrostatic distension, in endolymphatic hydrops in humans challenges the classic view of Meniere's disease.

Scientific reports
Meniere's disease (MD), a degenerative inner ear disorder, is characterized by debilitating episodic vertigo and hearing fluctuations, progressing to permanent sensory impairment. The prevailing dogma attributes these symptoms to abnormal inner ear f...

A data-driven machine learning framework to predict side effects of AstraZeneca and sinopharm COVID-19 vaccines.

Scientific reports
Due to the widespread COVID-19 vaccinations, we are focusing more on side effects to immunizations that might affect people's perceptions, and ultimately vaccine hesitancy. Machine learning (ML)-based predictive models using individual-level data ser...

Prognostic value of combining nutritional inflammatory index trajectories and tumor characteristics in cervical cancer.

BMC women's health
OBJECTIVE: This investigation seeks to examine how varying longitudinal patterns in nutritional inflammatory index (NII) correlate with clinical outcomes in cervical cancer patients, while developing predictive models for prognosis.

Artificial intelligence in anesthesia: comparison of the utility of ChatGPT v/s google gemini large language models in pre-anesthetic education: content, readability and sentiment analysis.

BMC anesthesiology
BACKGROUND: Large Language Models (LLMs) such as ChatGPT and Google Gemini are increasingly explored for their potential in patient education, particularly in the perioperative setting. As text-based tools trained on extensive datasets, they can gene...

Using machine learning for detection of Parkinson's disease and mild cognitive impairment.

PloS one
BACKGROUND: Parkinson's disease is a movement disorder featuring motor symptoms and cognitive decline, which can manifest as mild cognitive impairment. The incidence of mild cognitive impairment increases with disease progression, and Parkinson's dis...

ATR-FTIR Spectroscopy of Saliva and Machine Learning as a Screening Test for Sjögren Disease.

Analytical chemistry
Sjögren's disease is often an underdiagnosed autoimmune condition that primarily affects the exocrine glands, resulting in symptoms such as dry eyes and dry mouth. Diagnostic challenges stem from nonspecific symptoms, the absence of definitive biomar...

Using Machine Learning Methods to Examine Turnover Rates in State Health Agencies.

Journal of public health management and practice : JPHMP
CONTEXT: High turnover rates in the public health workforce pose ongoing challenges to maintain essential services and institutional knowledge. Recent studies indicate that job dissatisfaction, burnout, and structural barriers have intensified follow...

Machine learning-based management of hypertensive disorders in pregnancy: analysis of differences in key risk factors between gestational hypertension and pre-eclampsia and construction of a pre-eclampsia prediction model.

European journal of medical research
OBJECTIVES: It remains debated whether gestational hypertension (GH) and pre-eclampsia (PE) are distinct entities or different spectra of the same disease. Currently, comparative studies of risk factors for GH and PE in the same population are limite...