Primary Care

Application of Artificial Intelligence (AI) in Health Promotion: A Case Study of an Experience From a Public Health Institution in Sri Lanka.

This case study explores the application of artificial intelligence (AI)-based technologies by the Health Promotion Bureau, one of the main preventive health institutions in Sri Lanka. Public engagement was analyzed via randomly selected posts created via AI-based and non-AI-based technologies on the basis of their reach and engagement. The use of AI-generated images for health communication on so...

Disparate Model Performance and Stability in Machine Learning Clinical Support for Diabetes and Heart Diseases.

Machine Learning (ML) algorithms are vital for supporting clinical decision-making in biomedical informatics. However, their predictive performance can vary across demographic groups, often due to the underrepresentation of historically marginalized populations in training datasets. The investigation reveals widespread sex- and age-related inequities in chronic disease datasets and their derived M...

Explainable Diagnosis Prediction through Neuro-Symbolic Integration.

Diagnosis prediction is a critical task in healthcare, where timely and accurate identification of medical conditions can significantly impact patient...

Screening of Biomaterials for Stem Cell Culture Applications.

Stem cells have a considerable role to play in future biomedical breakthroughs due to their therapeutic potential. As stem cells may be studied in a v...

Identification and validation of blood leukocyte DNA methylation biomarkers for early detection of colorectal neoplasm.

BACKGROUND: Identifying high-risk populations for colorectal cancer (CRC) is critical for precise screening. This study aimed to develop a novel risk ...

Identification of macrophage-associated diagnostic biomarkers and molecular subtypes in gestational diabetes mellitus based on machine learning.

Gestational diabetes mellitus (GDM) is a common metabolic disorder during pregnancy, involving multiple immune and inflammatory factors. Macrophages p...

Evaluating the relationship between environmental chemicals and obesity: Evidence from a machine learning perspective.

Environmental chemicals are increasingly recognized as important contributors to obesity, yet the number of studies evaluating this relationship remai...

Interpretable machine learning models for predicting childhood myopia from school-based screening data.

This study assessed the efficacy of various diagnostic indicators and machine learning (ML) models in predicting childhood myopia. A total of 2,365 ch...

Enhancing Antidiabetic Drug Selection Using Transformers: Machine-Learning Model Development.

BACKGROUND: Diabetes affects millions worldwide. Primary care physicians provide a significant portion of care, and they often struggle with selecting...

Out-of-distribution reject option method for dataset shift problem in early disease onset prediction.

Machine learning is increasingly used to predict lifestyle-related disease onset using health and medical data. However, its predictive accuracy for u...

Data-driven diabetes mellitus prediction and management: a comparative evaluation of decision tree classifier and artificial neural network models along with statistical analysis.

Diabetes Mellitus is a chronic metabolic disorder affecting a substantial global population leading to complications such as retinopathy, nephropathy,...

Skin cancer segmentation and classification by implementing a hybrid FrCN-(U-NeT) technique with machine learning.

Skin cancer is a severe and rapidly advancing condition that can be impacted by multiple factors, including alcohol and tobacco use, allergies, infect...

Assessing the stress hyperglycemia ratio in hyperlipidemia patients to predict all-cause mortality: A retrospective cohort study.

BACKGROUND: The Stress Hyperglycemia Ratio (SHR) reflects stress-related hyperglycemia and is linked to poor outcomes in various diseases. This study ...

Explainable Artificial Intelligence in Radiological Cardiovascular Imaging-A Systematic Review.

Artificial intelligence (AI) and deep learning are increasingly applied in cardiovascular imaging. However, the "black box" nature of these models ra...

Development and Validation of a Lifestyle-Based 10-Year Risk Prediction Model of Colorectal Cancer for Early Stratification: Evidence from a Longitudinal Screening Cohort in China.

Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, with growing evidence linking risk to lifestyle and ...

Methodological Review of Classification Trees for Risk Stratification: An Application Example in the Obesity Paradox.

BACKGROUND: Classification trees (CTs) are widely used machine learning algorithms with growing applications in clinical research, especially for risk...

Revolutionizing diabetes care: the role of artificial intelligence in prevention, diagnosis, and patient care.

UNLABELLED: Millions of people worldwide have diabetes, a disease that is becoming more common and has substantial socioeconomic costs. Artificial int...

Screening/Diagnosing Sarcopenia with Machine Learning-Powered Risk Assessment: The SARCO X Study.

OBJECTIVES: Sarcopenia imposes significant morbidity and economic burden on health care systems, underscoring the critical need for early/effective sc...

Application of machine learning algorithms to identify risk factors for depression in type 2 diabetes mellitus patients: A Taiwan diabetes registry study.

BACKGROUND: We analyzed variables reported during routine clinical practice using a registrational database to estimate risk factors for depression in...

Evaluating the impact of metabolic indicators and scores on cardiovascular events using machine learning.

Cardiovascular diseases such as coronary artery disease, myocardial infarction, and heart failure impact millions of people annually globally and are ...