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Leveraging machine learning for duration of surgery prediction in knee and hip arthroplasty - a development and validation study.
BMC medical informatics and decision making
Mar 3, 2025
BACKGROUND: Duration of surgery (DOS) varies substantially for patients with hip and knee arthroplasty (HA/KA) and is a major risk factor for adverse events. We therefore aimed (1) to identify whether machine learning can predict DOS in HA/KA patient...
An interpretable machine learning model with demographic variables and dietary patterns for ASCVD identification: from U.S. NHANES 1999-2018.
BMC medical informatics and decision making
Mar 3, 2025
Current research on the association between demographic variables and dietary patterns with atherosclerotic cardiovascular disease (ASCVD) is limited in breadth and depth. This study aimed to construct a machine learning (ML) algorithm that can accur...
Explainable machine learning model for predicting acute pancreatitis mortality in the intensive care unit.
BMC gastroenterology
Mar 3, 2025
BACKGROUND: Current prediction models are suboptimal for determining mortality risk in patients with acute pancreatitis (AP); this might be improved by using a machine learning (ML) model. In this study, we aimed to construct an explainable ML model ...
Integrating radiomics into predictive models for low nuclear grade DCIS using machine learning.
Scientific reports
Mar 3, 2025
Predicting low nuclear grade DCIS before surgery can improve treatment choices and patient care, thereby reducing unnecessary treatment. Due to the high heterogeneity of DCIS and the limitations of biopsies in fully characterizing tumors, current dia...
MRI radiomics based on machine learning in high-grade gliomas as a promising tool for prediction of CD44 expression and overall survival.
Scientific reports
Mar 3, 2025
We aimed to predict CD44 expression and assess its prognostic significance in patients with high-grade gliomas (HGG) using non-invasive radiomics models based on machine learning. Enhanced magnetic resonance imaging, along with the corresponding gene...
Machine learning identifies clinical tumor mutation landscape pathways of resistance to checkpoint inhibitor therapy in NSCLC.
Journal for immunotherapy of cancer
Mar 3, 2025
BACKGROUND: Immune checkpoint inhibitors (CPIs) have revolutionized cancer therapy for several tumor indications. However, a substantial fraction of patients treated with CPIs derive no benefit or have short-lived responses to CPI therapy. Identifyin...
Does the alternating timing of rTMS combined with soft-hand rehabilitation robot affect the recovery of hand function in patients after stroke? A study protocol for a multicentre randomised controlled trial.
BMJ open
Mar 3, 2025
INTRODUCTION: Combining repetitive transcranial magnetic stimulation (rTMS) with robotic training could result in more significant improvements in motor function than either treatment alone. The efficacy of this combination may depend on the sequenci...
Performance of a point-of-care ultrasound platform for artificial intelligence-enabled assessment of pulmonary B-lines.
Cardiovascular ultrasound
Mar 3, 2025
BACKGROUND: The incorporation of artificial intelligence (AI) into point-of-care ultrasound (POCUS) platforms has rapidly increased. The number of B-lines present on lung ultrasound (LUS) serve as a useful tool for the assessment of pulmonary congest...
Interpretable machine learning model for early morbidity risk prediction in patients with sepsis-induced coagulopathy: a multi-center study.
Frontiers in immunology
Mar 3, 2025
BACKGROUND: Sepsis-induced coagulopathy (SIC) is a complex condition characterized by systemic inflammation and coagulopathy. This study aimed to develop and validate a machine learning (ML) model to predict SIC risk in patients with sepsis.
Texture-based probability mapping for automatic assessment of myocardial injury in late gadolinium enhancement images after revascularized STEMI.
International journal of cardiology
Mar 2, 2025
BACKGROUND: Late Gadolinium-enhancement in cardiac magnetic resonance imaging (LGE-CMR) is the gold standard for assessing myocardial infarction (MI) size. Texture-based probability mapping (TPM) is a novel machine learning-based analysis of LGE imag...
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