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Comparative analysis of machine learning models for coronary artery disease prediction with optimized feature selection.
International journal of cardiology
May 31, 2025
BACKGROUND: Coronary artery disease (CAD) is a major global cause of death, necessitating early, accurate prediction for better management. Traditional diagnostics are often invasive, costly, and less accessible. Machine learning (ML) offers a non-in...
Development and Validation of a Lifestyle-Based 10-Year Risk Prediction Model of Colorectal Cancer for Early Stratification: Evidence from a Longitudinal Screening Cohort in China.
Nutrients
May 31, 2025
Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, with growing evidence linking risk to lifestyle and dietary factors. However, nutrition-related exposures have rarely been integrated into existing CRC ...
Estimating motor symptom presence and severity in Parkinson's disease from wrist accelerometer time series using ROCKET and InceptionTime.
Scientific reports
May 31, 2025
Parkinson's disease (PD) is a neurodegenerative condition characterized by frequently changing motor symptoms, necessitating continuous symptom monitoring for more targeted treatment. Classical time series classification and deep learning techniques ...
Incorporating the STOP-BANG questionnaire improves prediction of cardiovascular events during hospitalization after myocardial infarction.
Scientific reports
May 31, 2025
Obstructive sleep apnea (OSA) may impact outcomes in acute coronary syndrome (ACS) patients. The Global Registry of Acute Coronary Events (GRACE) score assesses cardiovascular risk post-ACS. This study evaluated whether incorporating the STOP-BANG sc...
NeoPred: dual-phase CT AI forecasts pathologic response to neoadjuvant chemo-immunotherapy in NSCLC.
Journal for immunotherapy of cancer
May 31, 2025
BACKGROUND: Accurate preoperative prediction of major pathological response or pathological complete response after neoadjuvant chemo-immunotherapy remains a critical unmet need in resectable non-small-cell lung cancer (NSCLC). Conventional size-base...
Development Of the VAMPCT Score for Predicting Mortality in CKD Patients with COVID-19.
International journal of medical sciences
May 31, 2025
Chronic kidney disease (CKD) patients with coronavirus disease 2019 (COVID-19) are at significant risk of death. However, clinical identification of high-risk individuals remains suboptimal despite the recognition of many pathophysiological and como...
Deep learning reconstruction for T2-weighted and contrast-enhanced T1-weighted magnetic resonance enterography imaging in patients with Crohn's disease: Assessment of image quality and clinical utility.
Clinical imaging
May 30, 2025
PURPOSE: To investigate the image quality of deep learning-reconstructed T2-weighted half-Fourier single-shot turbo spin echo (DL T2 HASTE) and contrast-enhanced T1-weighted volumetric interpolated breath-hold examination (DL T1 VIBE) of magnetic res...
Prediction of Lymph Node Metastasis in Non-Small Cell Lung Carcinoma Using Primary Tumor Somatic Mutation Data.
JCO clinical cancer informatics
May 30, 2025
PURPOSE: Lymph node metastasis (LNM) significantly affects prognosis and treatment strategies in non-small cell lung cancer (NSCLC). Current diagnostic methods, including imaging and histopathology, have limited sensitivity and specificity. This stud...
Radiomics-based differentiation of upper urinary tract urothelial and renal cell carcinoma in preoperative computed tomography datasets.
BMC medical imaging
May 30, 2025
BACKGROUND: To investigate a non-invasive radiomics-based machine learning algorithm to differentiate upper urinary tract urothelial carcinoma (UTUC) from renal cell carcinoma (RCC) prior to surgical intervention.
Personalized medication recommendations for Parkinson's disease patients using gated recurrent units and SHAP interpretability.
Scientific reports
May 30, 2025
Managing Parkinson's disease (PD) through medication can be challenging due to varying symptoms and disease duration. This study aims to demonstrate the potential of sequence-by-sequence algorithms in recommending personalized medication combinations...
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