AI Medical Compendium Journal:
BMC medical informatics and decision making

Showing 91 to 100 of 718 articles

Development of an artificial intelligence-based application for the diagnosis of sarcopenia: a retrospective cohort study using the health examination dataset.

BMC medical informatics and decision making
BACKGROUND: Medical imaging techniques for diagnosing sarcopenia have been extensively investigated. Studies have proposed using the T-score and patient information as key diagnostic factors. However, these techniques have either been time-consuming ...

Examining inclusivity: the use of AI and diverse populations in health and social care: a systematic review.

BMC medical informatics and decision making
BACKGROUND: Artificial intelligence (AI)-based systems are being rapidly integrated into the fields of health and social care. Although such systems can substantially improve the provision of care, diverse and marginalized populations are often incor...

Sharing reliable information worldwide: healthcare strategies based on artificial intelligence need external validation. Position paper.

BMC medical informatics and decision making
Training machine learning models using data from severe COVID-19 patients admitted to a central hospital, where entire wards are specifically dedicated to COVID-19, may yield predictions that differ significantly from those generated using data colle...

Unlocking the link: predicting cardiovascular disease risk with a focus on airflow obstruction using machine learning.

BMC medical informatics and decision making
BACKGROUND: Respiratory diseases and Cardiovascular Diseases (CVD) often coexist, with airflow obstruction (AO) severity closely linked to CVD incidence and mortality. As both conditions rise, early identification and intervention in risk populations...

Developing clinical prognostic models to predict graft survival after renal transplantation: comparison of statistical and machine learning models.

BMC medical informatics and decision making
INTRODUCTION: Renal transplantation is a critical treatment for end-stage renal disease, but graft failure remains a significant concern. Accurate prediction of graft survival is crucial to identify high-risk patients. This study aimed to develop pro...

A machine learning-based model for predicting paroxysmal and persistent atrial fibrillation based on EHR.

BMC medical informatics and decision making
BACKGROUND: There is no effective way to accurately predict paroxysmal and persistent atrial fibrillation (AF) subtypes unless electrocardiogram (ECG) observation is obtained. We aim to develop a predictive model using a machine learning algorithm fo...

Towards unbiased skin cancer classification using deep feature fusion.

BMC medical informatics and decision making
This paper introduces SkinWiseNet (SWNet), a deep convolutional neural network designed for the detection and automatic classification of potentially malignant skin cancer conditions. SWNet optimizes feature extraction through multiple pathways, emph...

Exploring the assessment of post-cardiac valve surgery pulmonary complication risks through the integration of wearable continuous physiological and clinical data.

BMC medical informatics and decision making
BACKGROUND: Postoperative pulmonary complications (PPCs) following cardiac valvular surgery are characterized by high morbidity, mortality, and economic cost. This study leverages wearable technology and machine learning algorithms to preoperatively ...

Optimizing hypoglycaemia prediction in type 1 diabetes with Ensemble Machine Learning modeling.

BMC medical informatics and decision making
BACKGROUND: Type 1 diabetes (T1D) is a chronic endocrine disorder characterized by high blood glucose levels, impacting millions of people globally. Its management requires intensive insulin therapy, frequent blood glucose monitoring, and lifestyle a...

Using a robust model to detect the association between anthropometric factors and T2DM: machine learning approaches.

BMC medical informatics and decision making
BACKGROUND: The aim of this study was to evaluate the potential models to determine the most important anthropometric factors associated with type 2 diabetes mellitus (T2DM).