COVID-19 Prognostic Models: A Pro-con Debate for Machine Learning vs. Traditional Statistics

Overview of attention for article published in Frontiers in Digital Health, December 2021

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In the top 25% of all research outputs scored by Altmetric
Among the highest-scoring outputs from this source (#43 of 883)
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https://t.co/X3tz7ux8R3 線形回帰と機械学習の特徴の違いについて線形回帰モデルは、根拠に基づく医療のランダム化比較試験の基盤として機能。機械学習は特定の高次元データセットから可能な限り最高の機能が、元の変数の複雑な関数であることが判明する可能性がある。

13 Jan 2022

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COVID-19 Prognostic Models: A Pro-con Debate for Machine Learning vs. Traditional Statistics https://t.co/OAqvkdnt8B

10 Jan 2022

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RT @YvesMulkers: COVID-19 Prognostic Models: A Pro-con Debate for #machinelearning vs. Traditional Statistics The SARS-CoV-2 virus, which c…

25 Dec 2021

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COVID-19 Prognostic Models: A Pro-con Debate for #machinelearning vs. Traditional Statistics The SARS-CoV-2 virus, which causes the COVID-19 pandemic, has had an unprecedented impact on healthcare requiring mult .. https://t.co/Mco2EQnp7f https://t.co/zLd5

25 Dec 2021

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