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Comparative study of XGBoost and logistic regression for predicting sarcopenia in postsurgical gastric cancer patients.
Scientific reports
Apr 14, 2025
The use of machine learning (ML) techniques, particularly XGBoost and logistic regression, to predict sarcopenia among postsurgical gastric cancer patients has gained significant attention in recent research. Sarcopenia, characterized by the progress...
TET2 gene mutation status associated with poor prognosis of transition zone prostate cancer: a retrospective cohort study based on whole exome sequencing and machine learning models.
Frontiers in endocrinology
Apr 14, 2025
BACKGROUND: Prostate cancer (PCa) in the transition zone (TZ) is uncommon and often poses challenges for early diagnosis, but its genomic determinants and therapeutic vulnerabilities remain poorly characterized.
c-Triadem: A constrained, explainable deep learning model to identify novel biomarkers in Alzheimer's disease.
PloS one
Apr 14, 2025
Alzheimer's disease (AD) is a neurodegenerative disorder that requires early diagnosis for effective management. However, issues with currently available diagnostic biomarkers preclude early diagnosis, necessitating the development of alternative bio...
A radiomics approach to distinguish Progressive Supranuclear Palsy Richardson's syndrome from other phenotypes starting from MR images.
Computer methods and programs in biomedicine
Apr 13, 2025
BACKGROUND AND OBJECTIVE: Progressive Supranuclear Palsy (PSP) is an uncommon neurodegenerative disorder with different clinical onset, including Richardson's syndrome (PSP-RS) and other variant phenotypes (vPSP). Recognising the clinical progression...
Deep learning-based identification of patients at increased risk of cancer using routine laboratory markers.
Scientific reports
Apr 12, 2025
Early screening for cancer has proven to improve the survival rate and spare patients from intensive and costly treatments due to late diagnosis. Cancer screening in the healthy population involves an initial risk stratification step to determine the...
AI-enabled opportunistic measurement of liver steatosis in coronary artery calcium scans predicts cardiovascular events and all-cause mortality: an AI-CVD study within the Multi-Ethnic Study of Atherosclerosis (MESA).
BMJ open diabetes research & care
Apr 12, 2025
INTRODUCTION: About one-third of adults in the USA have some grade of hepatic steatosis. Coronary artery calcium (CAC) scans contain more information than currently reported. We previously reported new artificial intelligence (AI) algorithms applied ...
Remdesivir associated with reduced mortality in hospitalized COVID-19 patients: treatment effectiveness using real-world data and natural language processing.
BMC infectious diseases
Apr 12, 2025
BACKGROUND: Remdesivir (RDV) was the first antiviral approved for mild-to-moderate COVID-19 and for those patients at risk for progression to severe disease after clinical trials supported its association with improved outcomes. Real-world evidence (...
Predicting prolonged length of in-hospital stay in patients with non-ST elevation myocardial infarction (NSTEMI) using artificial intelligence.
International journal of cardiology
Apr 11, 2025
BACKGROUND: Patients presenting with non-ST elevation myocardial infarction (NSTEMI) are typically evaluated using coronary angiography and managed through coronary revascularization. Numerous studies have demonstrated the benefits of expedited disch...
A comparison of an integrated and image-only deep learning model for predicting the disappearance of indeterminate pulmonary nodules.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Apr 11, 2025
BACKGROUND: Indeterminate pulmonary nodules (IPNs) require follow-up CT to assess potential growth; however, benign nodules may disappear. Accurately predicting whether IPNs will resolve is a challenge for radiologists. Therefore, we aim to utilize d...
External Validation of an Artificial Intelligence Algorithm Using Biparametric MRI and Its Simulated Integration with Conventional PI-RADS for Prostate Cancer Detection.
Academic radiology
Apr 11, 2025
PURPOSE: Prostate imaging reporting and data systems (PI-RADS) experiences considerable variability in inter-reader performance. Artificial Intelligence (AI) algorithms were suggested to provide comparable performance to PI-RADS for assessing prostat...
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