ML algorithms were trained using the earlier HRQoL assessment and clinical data to predict dichotomized impairments in QLQ-C30 domains at the later assessment between 2 weeks and 5 years ahead, ...
Polygenic risk scores (PRSs) aggregate genetic information to estimate individual predisposition to a trait. While most PRSs model the phenotypic mean, patterns of variability can also be informative ...
Background Patients with severe aortic stenosis (AS) are at high risk of mortality, regardless of symptom status. Despite this, aortic valve replacement rates remain low for patients with severe AS ...
aDepartment of Medicine, University of California San Francisco, San Francisco, CA, USA bDepartment of Medicine, University of California Los Angeles, Los Angeles, CA, USA cDepartment of Medicine, ...
Nanotechnology Research Laboratory, Research School of Chemistry, College of Science, The Australian National University, Canberra, ACT 2601, Australia School of Engineering and Technology, The ...
This model was trained and tested on a 70%/30% split (train/test result cohort), achieving an area under the receiver operator curve on the test set of 0.866 (95% CI, 0.857 to 0.875). Assigning a ...
Objectives This study aimed to employ machine learning algorithms to predict the factors contributing to zero-dose children in Tanzania, using the most recent nationally representative data. Design ...
Abstract: This study explores a machine learning approach for detecting and classifying fetal health which is a crucial aspect in ensuring proper care and wellbeing of both fetus and the mother. The ...
We hypothesized that analysis of serial ECGs could predict new‐onset atrial fibrillation (AF) more accurately than analysis of a single ECG by detecting the subtle cardiac remodeling that occurs ...
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