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机器学习模型用于计算跟踪和预测 COVID-19 动态传播。

Machine Learning Model for Computational Tracking and Forecasting the COVID-19 Dynamic Propagation.

出版信息

IEEE J Biomed Health Inform. 2021 Mar;25(3):615-622. doi: 10.1109/JBHI.2021.3052134. Epub 2021 Mar 5.

DOI:10.1109/JBHI.2021.3052134
PMID:33449891
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8545165/
Abstract

A computational model with intelligent machine learning for analysis of epidemiological data, is proposed. The innovations of adopted methodology consist of an interval type-2 fuzzy clustering algorithm based on adaptive similarity distance mechanism for defining specific operation regions associated to the behavior and uncertainty inherited to epidemiological data, and an interval type-2 fuzzy version of Observer/Kalman Filter Identification (OKID) algorithm for adaptive tracking and real time forecasting according to unobservable components computed by recursive spectral decomposition of experimental epidemiological data. Experimental results and comparative analysis illustrate the efficiency and applicability of proposed methodology for adaptive tracking and real time forecasting the dynamic propagation behavior of novel coronavirus 2019 (COVID-19) outbreak in Brazil.

摘要

提出了一种用于分析流行病学数据的具有智能机器学习的计算模型。所采用方法的创新之处在于，基于自适应相似性距离机制的区间型 2 模糊聚类算法，用于定义与流行病学数据的行为和不确定性相关的特定操作区域，以及区间型 2 模糊版本的 Observer/Kalman Filter Identification (OKID) 算法，用于根据实验流行病学数据的递归谱分解计算出的不可观测分量进行自适应跟踪和实时预测。实验结果和对比分析表明了所提出的方法对于自适应跟踪和实时预测巴西 2019 年新型冠状病毒（COVID-19）爆发的动态传播行为的有效性和适用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/206b/8545165/c4d396a61e2a/serra6-3052134.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/206b/8545165/5a6415575b09/serra1-3052134.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/206b/8545165/05928eb84a6e/serra2-3052134.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/206b/8545165/183f51bf1d6d/serra3-3052134.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/206b/8545165/131bbafd49a0/serra4-3052134.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/206b/8545165/ccf4a6a3b7d6/serra5-3052134.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/206b/8545165/c4d396a61e2a/serra6-3052134.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/206b/8545165/5a6415575b09/serra1-3052134.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/206b/8545165/05928eb84a6e/serra2-3052134.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/206b/8545165/183f51bf1d6d/serra3-3052134.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/206b/8545165/131bbafd49a0/serra4-3052134.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/206b/8545165/ccf4a6a3b7d6/serra5-3052134.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/206b/8545165/c4d396a61e2a/serra6-3052134.jpg

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3
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Artif Intell Med. 2022 Jun;128:102286. doi: 10.1016/j.artmed.2022.102286. Epub 2022 Mar 28.
4
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5
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