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cloud-based automated machine learning platform  (BigML Inc)

 
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    BigML Inc cloud-based automated machine learning platform
    Cloud Based Automated Machine Learning Platform, supplied by BigML Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/cloud-based+automated+machine+learning+platform/cloud+based+automated+machine+learning+platform/10__1016_slash_j__bspc__2024__106013-60-15-18
    Average 90 stars, based on 1 article reviews
    cloud-based automated machine learning platform - by Bioz Stars, 2026-11
    90/100 stars

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    other:

    Article Title: Dissecting the latent representation of age inside a deep neural network’s predictions of diastolic dysfunction using echocardiographic variables
    Article Snippet: After labeling the patients as high-risk or low-risk using the TDA-based approach, a cloud-based automated machinelearning platform (OptiML, BigML, Corvallis, Oregon) was used to train multiple machine learning algorithms and selected the best performing algorithm as the DeepNN model. Our group has already published the detailed methodologies for the TDA-based similarity network and the DeepNN model in [9,10].

    Selection:

    Article Title: A low-cost texture-based pipeline for predicting myocardial tissue remodeling and fibrosis using cardiac ultrasound
    Article Snippet: .. Using a cloud-based automated machine learning platform (OptiML, BigML.com, Corvallis, Oregon, http://bigml.com ), multiple algorithms, including decision trees, ensemble models, logistic regression models with least absolute shrinkage (LASSO) and selection operator, and deep neural networks, were trained to predict reduced LVEF (≤50%) and impaired GLS (<16% in absolute value). ..

    Labeling:

    Article Title: Deep-Learning Models for the Echocardiographic Assessment of Diastolic Dysfunction.
    Article Snippet: ISS Fro Te Mo D St. Va thi Jag OBJECTIVES The authors explored a deep neural network (DeepNN) model that integrates multidimensional echocardiographic data to identify distinct patient subgroups with heart failure with preserved ejection fraction (HFpEF).. BACKGROUND The clinical algorithms for phenotyping the severity of diastolic dysfunction in HFpEF remain imprecise.. METHODS The authors developed a DeepNN model to predict highand low-risk phenogroups in a derivation cohort (n1⁄4 1,242).



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