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Analyzing factors influencing hospitalization costs for five common cancers in China using neural network models.

Journal of medical economics
BACKGROUND: Malignant tumors are a major global health crisis, causing 25% of deaths in China, with lung, liver, thyroid, breast, and colon cancers being the most common. Understanding the factors influencing hospitalization costs for these cancers i...

Heart failure monitoring with a single‑lead electrocardiogram at home.

International journal of cardiology
BACKGROUND: Repeated hospitalization due to heart failure (HF) is a significant predictor of mortality. However, there are limited early detection systems for HF progression that can be utilized by patients at home without a cardiac implantable elect...

Artificial intelligence and machine learning in ocular oncology, retinoblastoma (ArMOR).

Indian journal of ophthalmology
PURPOSE: To test the accuracy of a trained artificial intelligence and machine learning (AI/ML) model in the diagnosis and grouping of intraocular retinoblastoma (iRB) based on the International Classification of Retinoblastoma (ICRB) in a larger coh...

Optimising coronary imaging decisions with machine learning: an external validation study.

Open heart
BACKGROUND: Exclusion of coronary stenosis in individuals with suggestive symptoms is challenging. Cardiac CT or coronary angiography is often used but is inefficient and costly and involves risks. Sex-stratified algorithms based on electronic health...

Construction and validation of prognostic model for ICU mortality in cardiac arrest patients: an interpretable machine learning modeling approach.

European journal of medical research
BACKGROUND: The incidence and mortality of cardiac arrest (CA) is high. We developed interpretable machine learning models for early prediction of ICU mortality risk in patients diagnosed with CA.

SEM model analysis of diabetic patients' acceptance of artificial intelligence for diabetic retinopathy.

BMC medical informatics and decision making
AIMS: This study aimed to investigate diabetic patients' acceptance of artificial intelligence (AI) devices for diabetic retinopathy screening and the related influencing factors.

Digital and artificial intelligence-assisted cephalometric training effectively enhanced students' landmarking accuracy in preclinical orthodontic education.

BMC oral health
BACKGROUND: Digital cephalometric analyses, including those assisted by artificial intelligence (AI), are widely used in clinical practice. Similarly, computer-assisted learning has demonstrated teaching outcomes comparable to those of traditional me...

Generative artificial intelligence in physiotherapy education: great potential amidst challenges- a qualitative interview study.

BMC medical education
BACKGROUND: Generative Artificial Intelligence (GAI) has significantly impacted education at all levels, including health professional education. Understanding students' experiences is essential to enhancing AI literacy, adapting education to GAI, an...

Prediction of significant congenital heart disease in infants and children using continuous wavelet transform and deep convolutional neural network with 12-lead electrocardiogram.

BMC pediatrics
BACKGROUND: Congenital heart disease (CHD) affects approximately 1% of newborns and is a leading cause of mortality in early childhood. Despite the importance of early detection, current screening methods, such as pulse oximetry and auscultation, hav...

Constructing machine learning-based risk prediction model for osteoarthritis in population aged 45 and above: NHANES 2011-2018.

Scientific reports
Osteoarthritis is a widespread chronic joint disease, becoming increasingly prevalent, particularly among individuals over the age of 45. This condition causes joint pain and dysfunction, significantly disrupting daily life. The objective of this stu...