AIMC Topic: Cross-Sectional Studies
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Machine learning in lymphocyte and immune biomarker analysis for childhood thyroid diseases in China.
BMC pediatrics
Mar 28, 2025
OBJECTIVE: This study aims to characterize and analyze the expression of representative biomarkers like lymphocytes and immune subsets in children with thyroid disorders. It also intends to develop and evaluate a machine learning model to predict if ...
Interpretable machine learning method to predict the risk of pre-diabetes using a national-wide cross-sectional data: evidence from CHNS.
BMC public health
Mar 26, 2025
OBJECTIVE: The incidence of Type 2 Diabetes Mellitus (T2DM) continues to rise steadily, significantly impacting human health. Early prediction of pre-diabetic risks has emerged as a crucial public health concern in recent years. Machine learning meth...
Perceived artificial intelligence readiness in medical and health sciences education: a survey study of students in Saudi Arabia.
BMC medical education
Mar 26, 2025
BACKGROUND: As artificial intelligence (AI) becomes increasingly integral to healthcare, preparing medical and health sciences students to engage with AI technologies is critical.
Nutritional predictors of lymphatic filariasis progression: Insights from a machine learning approach.
PloS one
Mar 26, 2025
Lymphatic filariasis (LF) is a mosquito-borne neglected tropical disease that causes disfiguring of the affected extremities, often leading to permanent disability and stigma. Described as a disease of poverty, the impact of socioeconomic indicators ...
Convolutional Neural Network Models for Visual Classification of Pressure Ulcer Stages: Cross-Sectional Study.
JMIR medical informatics
Mar 25, 2025
BACKGROUND: Pressure injuries (PIs) pose a negative health impact and a substantial economic burden on patients and society. Accurate staging is crucial for treating PIs. Owing to the diversity in the clinical manifestations of PIs and the lack of ob...
Post-Anesthesia Care Unit (PACU) readiness predictions using machine learning: a comparative study of algorithms.
BMC medical informatics and decision making
Mar 25, 2025
INTRODUCTION: Accurate and timely discharge from the Post-Anesthesia Care Unit (PACU) is essential to prevent postoperative complications and optimize hospital resource utilization. Premature discharge can lead to severe issues such as respiratory or...
Effectiveness of AI-generated orthodontic treatment plans compared to expert orthodontist recommendations: a cross-sectional pilot study.
Dental press journal of orthodontics
Mar 24, 2025
INTRODUCTION: Artificial intelligence (AI) has become a prominent focus in orthodontics.
Which approach better predicts diabetes: Traditional econometric methods or machine learning? Evidence from a cross-sectional study in South Korea.
Computers in biology and medicine
Mar 23, 2025
To prevent chronic disease from getting worse, it is important to detect and predict it at an early stage. Therefore, the accuracy of the prediction is particularly important. To investigate the accuracy of different methods, this study compares the ...
Prediction of zero-dose children using supervised machine learning algorithm in Tanzania: evidence from the recent 2022 Tanzania Demographic and Health Survey.
BMJ open
Mar 22, 2025
OBJECTIVES: This study aimed to employ machine learning algorithms to predict the factors contributing to zero-dose children in Tanzania, using the most recent nationally representative data.
Learning from the machine: is diabetes in adults predicted by lifestyle variables? A retrospective predictive modelling study of NHANES 2007-2018.
BMJ open
Mar 22, 2025
OBJECTIVES: This study aimed to compare the performance of five machine learning algorithms to predict diabetes mellitus based on lifestyle factors (diet and physical activity).
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