BACKGROUND: Surgical mechanical ventricular assistance and cardiac replacement therapies, although life-saving in many heart failure (HF) patients, remain high-risk. Despite this, the difficulty in timely identification of medical therapy nonresponde...
In this paper, we present a methodology based on generative adversarial network architecture to generate synthetic data sets with the intention of augmenting continuous glucose monitor data from individual patients. We use these synthetic data with t...
The timely identification of cohort participants at higher risk for attrition is important to earlier interventions and efficient use of research resources. Machine learning may have advantages over the conventional approaches to improve discriminati...
International journal of colorectal disease
Jun 16, 2022
PURPOSE: Evidence regarding local recurrence rates in the initial cases after implementation of robot-assisted total mesorectal excision is limited. This study aims to describe local recurrence rates in four large Dutch centres during their initial c...
INTRODUCTION: We aimed to examine the relationship between D'Amico intermediate-risk and pathological grade group 1 (pGG1) after robot-assisted radical prostatectomy (RARP).
Alzheimer's & dementia : the journal of the Alzheimer's Association
Jun 6, 2022
INTRODUCTION: To test the utility of the "A/T/N" system in the Chinese population, we study core Alzheimer's disease (AD) biomarkers in a newly established Chinese cohort.
We sought to predict whether central serous chorioretinopathy (CSC) will persist after 6 months using multiple optical coherence tomography (OCT) images by deep convolutional neural network (CNN). This was a multicenter, retrospective, cohort study. ...
We report on the first clinical experience with the robotic-assisted extended "Sistrunk" approach (RESA) for access to constrained spaces of the upper aerodigestive tract. This prospective case cohort study include six patients that underwent RESA if...
BACKGROUNDS: We aimed to develop and validate machine learning (ML) models for 30-day stroke mortality for mortality risk stratification and as benchmarking models for quality improvement in stroke care.
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