AIMC Topic: Cross-Sectional Studies

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Comparison of Machine Learning Algorithms and Bayesian Estimation in Predicting Tacrolimus Concentration in Tunisian Kidney Transplant Patients During the Early Post-Transplant Period.

European journal of drug metabolism and pharmacokinetics
BACKGROUND AND OBJECTIVE: Model-informed precision dosing (MIPD), based on a Bayesian approach and machine learning (ML) algorithms, is a suitable approach to personalize dosage recommendations and to improve the concentration target attainment for e...

Psychological hardiness, sleepiness, and fatigue as predictors of occupational errors in nurses: implications for enhancing nurse well-being and patient safety.

Industrial health
Nurses are at increased risk of making professional errors due to a combination of interrelated factors. We investigated the effects of sleepiness, fatigue, psychological hardiness, and demographic factors on the frequency of medical errors among act...

Systemic inflammation mediates the relationship between urinary cadmium and chronic cough risk: findings based on multiple statistical models.

Biometals : an international journal on the role of metal ions in biology, biochemistry, and medicine
Epidemiological research examining the relationship between urinary cadmium and the risk of chronic cough remains scarce. This study included 2965 participants for a cross-sectional study from the NHANES. The weighted quantile sum (WQS) regression, b...

Machine Learning Models Can Predict Tinnitus and Noise-Induced Hearing Loss.

Ear and hearing
OBJECTIVES: Despite the extensive use of machine learning (ML) models in health sciences for outcome prediction and condition classification, their application in differentiating various types of auditory disorders remains limited. This study aimed t...

Referral patterns, influencing factors, and satisfaction related to referrals of patients with rheumatic diseases to other healthcare professionals: an online survey of rheumatologists.

Rheumatology international
Managing rheumatic diseases requires teamwork, but referral patterns and challenges remain poorly understood. This study explored rheumatologists' perspectives on referral patterns in the Gulf countries. We conducted a web-based, 21-question cross-se...

Is AI the future of evaluation in medical education?? AI vs. human evaluation in objective structured clinical examination.

BMC medical education
BACKGROUND: Objective Structured Clinical Examinations (OSCEs) are widely used in medical education to assess students' clinical and professional skills. Recent advancements in artificial intelligence (AI) offer opportunities to complement human eval...

The Relationship Between Anxiety and Readiness Levels Regarding Artificial Intelligence in Midwives: An Intergenerational Comparative Study.

Computers, informatics, nursing : CIN
This study aimed to compare Generations X, Y, and Z in terms of anxiety and readiness levels regarding artificial intelligence and investigate the relationship between anxiety and readiness levels regarding artificial intelligence in midwives across ...

An artificial intelligence malnutrition screening tool based on electronic medical records.

Clinical nutrition ESPEN
BACKGROUND & AIMS: Nutrition screening is a fundamental step to ensure appropriate intervention in patients with malnutrition. An automatic tool of nutritional risk screening based on electronic health records will improve efficiency and elevate the ...

Machine Learning-Driven Identification of Hematological and Immunological Biomarkers for Predicting Proliferative Diabetic Retinopathy Progression.

Current eye research
PURPOSE: Proliferative Diabetic Retinopathy (PDR) is a severe complication of diabetes characterized by neovascularization and retinal detachment, leading to significant vision loss. This study investigates the predictive power of hematological and i...