This study aimed to design a desktop application that implements machine learning algorithms to predict dental treatment time durations, assess the accuracy of the model, and assess its clinical efficiency. The Python programming language was used to...
OBJECTIVE: To explore the mechanisms of acupuncture-induced neck pain relief and identify appropriate candidates using neuroimaging and machine learning techniques.
BACKGROUND: Existing biomarkers for epithelial ovarian cancer (EOC) have demonstrated limited sensitivity and specificity. This study aimed to investigate plasma protein and metabolite characteristics of EOC and identify novel biomarker candidates fo...
Cardiotoxicity is the loss of the heart muscle's ability to contract effectively, often due to chemotherapy or radiation therapy. This study uses interpretable machine learning to predict post-chemotherapy cardiotoxicity using radiomics features extr...
Heart failure (HF) is a condition with periods of stability interrupted by periods of worsening symptoms, known as decompensation episodes. Digital interventions are promising tools to alleviate burdens on HF management through automated alerts at th...
Previous studies have found that major depressive disorder (MDD) may accelerate overall structural brain aging. Nevertheless, it still remains unknown whether anhedonia, a critical negative prognostic indicator in MDD, further leads to advanced brain...
BACKGROUND: There is a lack of atrial fibrillation (AF) prediction models tailored for individuals without prior cardiovascular diseases (CVDs) to facilitate early intervention. This study aimed to develop and validate an AF prediction model using ma...
INTRODUCTION: This study seeks to determine incidence, comorbidities and drivers for new HIV infections to develop, test and validate a risk prediction model for screening for new cases of HIV.
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