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Investigating factors influencing quality of life in thyroid eye disease: insight from machine learning approaches.

European thyroid journal
AIMS: Thyroid eye disease (TED) is an autoimmune orbital disorder that diminishes the quality of life (QOL) in affected individuals. Graves' ophthalmopathy (GO)-QOL questionnaire effectively assesses TED's effect on patients. This study aims to inves...

Prediction of urinary tract infection using machine learning methods: a study for finding the most-informative variables.

BMC medical informatics and decision making
BACKGROUND: Urinary tract infection (UTI) is a frequent health-threatening condition. Early reliable diagnosis of UTI helps to prevent misuse or overuse of antibiotics and hence prevent antibiotic resistance. The gold standard for UTI diagnosis is ur...

A machine learning model accurately identifies glycogen storage disease Ia patients based on plasma acylcarnitine profiles.

Orphanet journal of rare diseases
BACKGROUND: Glycogen storage disease (GSD) Ia is an ultra-rare inherited disorder of carbohydrate metabolism. Patients often present in the first months of life with fasting hypoketotic hypoglycemia and hepatomegaly. The diagnosis of GSD Ia relies on...

Development and external validation of a machine learning model for brain injury in pediatric patients on extracorporeal membrane oxygenation.

Critical care (London, England)
BACKGROUND: Patients supported by extracorporeal membrane oxygenation (ECMO) are at a high risk of brain injury, contributing to significant morbidity and mortality. This study aimed to employ machine learning (ML) techniques to predict brain injury ...

Machine-learning-based cost prediction models for inpatients with mental disorders in China.

BMC psychiatry
BACKGROUND: Mental disorders are increasingly prevalent, leading to increased medical expenditures. To refine the reimbursement of medical costs for inpatients with mental disorders by health insurance, an accurate prediction model is essential. Per-...

A robust multimodal brain MRI-based diagnostic model for migraine: validation across different migraine phases and longitudinal follow-up data.

The journal of headache and pain
Inter-individual variability in symptoms and the dynamic nature of brain pathophysiology present significant challenges in constructing a robust diagnostic model for migraine. In this study, we aimed to integrate different types of magnetic resonance...

The impact of preschool children's physical fitness evaluation under self organizing maps neural network.

Scientific reports
To improve the scientific accuracy and precision of children's physical fitness evaluations, this study proposes a model that combines self-organizing maps (SOM) neural networks with cluster analysis. Existing evaluation methods often rely on traditi...

Using XBGoost, an interpretable machine learning model, for diagnosing prostate cancer in patients with PSA < 20 ng/ml based on the PSAMR indicator.

Scientific reports
To create a diagnostic tool before biopsy for patients with prostate-specific antigen (PSA) levels < 20 ng/ml to minimize prostate biopsy-related discomfort and risks. Data from 655 patients who underwent transperineal prostate biopsy at the First Af...

The importance of clinical experience in AI-assisted corneal diagnosis: verification using intentional AI misleading.

Scientific reports
We developed an AI system capable of automatically classifying anterior eye images as either normal or indicative of corneal diseases. This study aims to investigate the influence of AI's misleading guidance on ophthalmologists' responses. This cross...

Machine learning algorithms for predicting delayed hyponatremia after transsphenoidal surgery for patients with pituitary adenoma.

Scientific reports
This study aimed to develop and validate machine learning (ML) models to predict the occurrence of delayed hyponatremia after transsphenoidal surgery for pituitary adenoma. We retrospectively collected clinical data on patients with pituitary adenoma...