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28-day all-cause mortality in patients with alcoholic cirrhosis: a machine learning prediction model based on the MIMIC-IV.
Clinical and experimental medicine
Jun 11, 2025
To develop and validate a machine learning prediction model for 28-day all-cause mortality in patients with alcoholic cirrhosis using data from the MIMIC-IV database. The data of 2134 patients diagnosed with alcoholic cirrhosis (AC) were obtained fro...
Deep learning neural network prediction of postoperative complications in patients undergoing laparoscopic right hemicolectomy with or without CME and CVL for colon cancer: insights from SICE (Società Italiana di Chirurgia Endoscopica) CoDIG data.
Techniques in coloproctology
Jun 11, 2025
BACKGROUND: Postoperative complications in colorectal surgery can significantly impact patient outcomes and healthcare costs. Accurate prediction of these complications enables targeted perioperative management, improving patient safety and optimizin...
Next-Generation Sequencing-Based Testing Among Patients With Advanced or Metastatic Nonsquamous Non-Small Cell Lung Cancer in the United States: Predictive Modeling Using Machine Learning Methods.
JMIR cancer
Jun 11, 2025
BACKGROUND: Next-generation sequencing (NGS) has become a cornerstone of treatment for lung cancer and is recommended in current treatment guidelines for patients with advanced or metastatic disease.
Extremity Soft Tissue Sarcoma Reconstruction Nomograms: A Clinicoradiomic, Machine Learning-Powered Predictor of Postoperative Outcomes.
JCO clinical cancer informatics
Jun 11, 2025
PURPOSE: The choice of wound closure modality after limb-sparing extremity soft-tissue sarcoma (eSTS) resection is fraught with uncertainty. Leveraging machine learning and clinicoradiomic data, we developed Sarcoma Reconstruction Nomograms (SARCON),...
An ensemble-based 3D residual network for the classification of Alzheimer's disease.
PloS one
Jun 11, 2025
Alzheimer's disease (AD) is a common type of dementia, with mild cognitive impairment (MCI) being a key precursor. Early MCI diagnosis is crucial for slowing AD progression, but distinguishing MCI from normal controls (NC) is challenging due to subtl...
Deep learning-based post-hoc noise reduction improves quarter-radiation-dose coronary CT angiography.
European journal of radiology
Jun 9, 2025
PURPOSE: To evaluate the impact of deep learning-based post-hoc noise reduction (DLNR) on image quality, coronary artery disease reporting and data system (CAD-RADS) assessment, and diagnostic performance in quarter-dose versus full-dose coronary CT ...
Differentiating Bacterial and Non-Bacterial Pneumonia on Chest CT Using Multi-Plane Features and Clinical Biomarkers.
Academic radiology
Jun 9, 2025
RATIONALE AND OBJECTIVES: Timely and accurate classification of bacterial pneumonia (BP) is essential for guiding antibiotic therapy. However, distinguishing BP from non-bacterial pneumonia (NBP) using computed tomography (CT) is challenging due to o...
Explainable machine learning model for predicting internal mammary node metastasis in breast cancer: Multi-method development and cross-cohort validation.
Breast (Edinburgh, Scotland)
Jun 9, 2025
BACKGROUND: This study developed an explainable machine learning model for baseline internal mammary lymph node metastasis (IMNM) in breast cancer patients.
Artificial intelligence-assisted diagnosis and prognostication in low ejection fraction using electrocardiograms in inpatient department: a pragmatic randomized controlled trial.
BMC medicine
Jun 9, 2025
BACKGROUND: Early diagnosis of low ejection fraction (EF) remains challenging despite being a treatable condition. This study aimed to evaluate the effectiveness of an electrocardiogram (ECG)-based artificial intelligence (AI)-assisted clinical decis...
Investigating methods to enhance interpretability and performance in cardiac MRI for myocardial scarring diagnosis using convolutional neural network classification and One Match.
PloS one
Jun 9, 2025
Machine learning (ML) classification of myocardial scarring in cardiac MRI is often hindered by limited explainability, particularly with convolutional neural networks (CNNs). To address this, we developed One Match (OM), an algorithm that builds on ...
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