Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future infer the present and phenotype

机译：机械式机器学习：数据同化如何使用贝叶斯推断来预测未来推断当前和表型如何利用生理知识

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摘要

We introduce data assimilation as a computational method that uses machine learning to combine data with human knowledge in the form of mechanistic models in order to forecast future states, to impute missing data from the past by smoothing, and to infer measurable and unmeasurable quantities that represent clinically and scientifically important phenotypes. We demonstrate the advantages it affords in the context of type 2 diabetes by showing how data assimilation can be used to forecast future glucose values, to impute previously missing glucose values, and to infer type 2 diabetes phenotypes. At the heart of data assimilation is the mechanistic model, here an endocrine model. Such models can vary in complexity, contain testable hypotheses about important mechanics that govern the system (eg, nutrition’s effect on glucose), and, as such, constrain the model space, allowing for accurate estimation using very little data.

机译：我们引入数据同化作为一种计算方法，该方法使用机器学习以机械模型的形式将数据与人类知识相结合，以预测未来状态，通过平滑来估算过去的缺失数据，并推断可表示和可测量的数量临床和科学上重要的表型。我们通过展示如何利用数据同化来预测未来的葡萄糖值，估算先前缺失的葡萄糖值以及推断2型糖尿病表型，来证明其在2型糖尿病背景下的优势。数据吸收的核心是机制模型，这里是内分泌模型。此类模型的复杂程度可能有所不同，包含有关控制系统的重要机制（例如营养对葡萄糖的影响）的可验证假设，并且因此限制了模型空间，从而允许使用很少的数据进行准确的估算。

著录项

期刊名称 Journal of the American Medical Informatics Association : JAMIA
作者
David J Albers; Matthew E Levine; Andrew Stuart; Lena Mamykina; Bruce Gluckman; George Hripcsak;
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作者单位

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年(卷),期 2018(25),10
年度 2018
页码 1392–1401
总页数 10
原文格式 PDF
正文语种
中图分类
关键词
data assimilation Bayesian inverse methods state space models self-monitoring data machine learning data mining type 2 diabetes Gaussian process model glucose forecasting precision medicine;

机译：数据同化;贝叶斯逆方法;状态空间模型;自我监测数据;机器学习;数据挖掘;2型糖尿病;高斯过程模型;葡萄糖预测;精密医学;
入库时间 2022-08-21 10:53:00

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专利

1. Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype [J] . David J Albers, Matthew E Levine, Andrew Stuart, Journal of the American Medical Informatics Association : . 2018,第10期

机译：机械机械学习：数据同化如何利用贝叶斯推理利用生理知识来预测未来，推断出现和表型
2. A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches [J] . Kharfan Majd, Chan Vicky Wing Kei, Firdolas Efendigil Tugba Annals of Operations Research . 2021,第1a2期

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3. Data Assimilation for Streamflow Forecasting Using Extreme Learning Machines and Multilayer Perceptrons [J] . Boucher M-A, Quilty J., Adamowski J. Water resources research . 2020,第6期

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4. Data Assimilation Versus Machine Learning: Comparative Study Of Fish Catch Forecasting [C] . Yuka Horiuchi, Yuya Kokaki, Tetsunori Kobayashi, OCEANS Conference . 2019

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5. Improving GWAS Phenotypes Through Bayesian and Machine Learning Approaches [D] . Shafquat, Afrah . 2020

机译：通过贝叶斯和机器学习方法改善GWAS表型
6. Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data [O] . Abhik Shah, Peter Woolf -1

机译：贝叶斯学习的Python环境：从知识和数据中推断贝叶斯网络的结构
7. Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype [O] . David J Albers, Matthew E Levine, Andrew Stuart, 2018

机译：机械机械学习：数据同化如何利用贝叶斯推理来预测未来，推断出现和表型的生理知识

Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future infer the present and phenotype

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