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Prospective and External Evaluation of a Machine Learning Model to Predict In-Hospital Mortality of Adults at Time of Admission.

JAMA network open
IMPORTANCE: The ability to accurately predict in-hospital mortality for patients at the time of admission could improve clinical and operational decision-making and outcomes. Few of the machine learning models that have been developed to predict in-h...

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns.

Nature communications
In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Can...

Human arm weight compensation in rehabilitation robotics: efficacy of three distinct methods.

Journal of neuroengineering and rehabilitation
BACKGROUND: Arm weight compensation with rehabilitation robots for stroke patients has been successfully used to increase the active range of motion and reduce the effects of pathological muscle synergies. However, the differences in structure, perfo...

Automatic detection of brachytherapy seeds in 3D ultrasound images using a convolutional neural network.

Physics in medicine and biology
A novel approach for automatic localization of brachytherapy seeds in 3D transrectal ultrasound (TRUS) images, using machine learning based algorithm, is presented. 3D radiofrequency ultrasound signals were collected from 13 patients using the linear...

The impact of different training load quantification and modelling methodologies on performance predictions in elite swimmers.

European journal of sport science
The use of rolling averages to analyse training data has been debated recently. We evaluated two training load quantification methods (five-zone, seven-zone) fitted to performances over two race distances (50 and 100 m) using four separate longitudin...

Virtual genetic diagnosis for familial hypercholesterolemia powered by machine learning.

European journal of preventive cardiology
AIMS: Familial hypercholesterolemia (FH) is the most common genetic disorder of lipid metabolism. The gold standard for FH diagnosis is genetic testing, available, however, only in selected university hospitals. Clinical scores - for example, the Dut...

Radiomics for classification of bone mineral loss: A machine learning study.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to develop predictive models to classify osteoporosis, osteopenia and normal patients using radiomics and machine learning approaches.

The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set.

International journal of medical informatics
INTRODUCTION: Research has shown that frailty, a geriatric syndrome associated with an increased risk of negative outcomes for older people, is highly prevalent among residents of residential aged care facilities (also called long term care facilitie...