The Experts below are selected from a list of 38982 Experts worldwide ranked by ideXlab platform
Samir E Abdelrahman - One of the best experts on this subject based on the ideXlab platform.
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learning hidden patterns from patient Multivariate Time Series data using convolutional neural networks a case study of healthcare cost prediction
Journal of Biomedical Informatics, 2020Co-Authors: Mohammad Amin Morid, Olivia Liu R Sheng, Kensaku Kawamoto, Samir E AbdelrahmanAbstract:Abstract Objective To develop an effective and scalable individual-level patient cost prediction method by automatically learning hidden temporal patterns from Multivariate Time Series data in patient insurance claims using a convolutional neural network (CNN) architecture. Methods We used three years of medical and pharmacy claims data from 2013 to 2016 from a healthcare insurer, where data from the first two years were used to build the model to predict costs in the third year. The data consisted of the Multivariate Time Series of cost, visit and medical features that were shaped as images of patients’ health status (i.e., matrices with Time windows on one dimension and the medical, visit and cost features on the other dimension). Patients’ Multivariate Time Series images were given to a CNN method with a proposed architecture. After hyper-parameter tuning, the proposed architecture consisted of three building blocks of convolution and pooling layers with an LReLU activation function and a customized kernel size at each layer for healthcare data. The proposed CNN learned temporal patterns became inputs to a fully connected layer. We benchmarked the proposed method against three other methods: (1) a spike temporal pattern detection method, as the most accurate method for healthcare cost prediction described to date in the literature; (2) a symbolic temporal pattern detection method, as the most common approach for leveraging healthcare temporal data; and (3) the most commonly used CNN architectures for image pattern detection (i.e., AlexNet, VGGNet and ResNet) (via transfer learning). Moreover, we assessed the contribution of each type of data (i.e., cost, visit and medical). Finally, we externally validated the proposed method against a separate cohort of patients. All prediction performances were measured in terms of mean absolute percentage error (MAPE). Results The proposed CNN configuration outperformed the spike temporal pattern detection and symbolic temporal pattern detection methods with a MAPE of 1.67 versus 2.02 and 3.66, respectively (p Conclusions Feature learning through the proposed CNN configuration significantly improved individual-level healthcare cost prediction. The proposed CNN was able to outperform temporal pattern detection methods that look for a pre-defined set of pattern shapes, since it is capable of extracting a variable number of patterns with various shapes. Temporal patterns learned from medical, visit and cost data made significant contributions to the prediction performance. Hyper-parameter tuning showed that considering three-month data patterns has the highest prediction accuracy. Our results showed that patients’ images extracted from Multivariate Time Series data are different from regular images, and hence require unique designs of CNN architectures. The proposed method for converting Multivariate Time Series data of patients into images and tuning them for convolutional learning could be applied in many other healthcare applications with Multivariate Time Series data.
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learning hidden patterns from patient Multivariate Time Series data using convolutional neural networks a case study of healthcare cost prediction
arXiv: Learning, 2020Co-Authors: Mohammad Amin Morid, Olivia Liu R Sheng, Kensaku Kawamoto, Samir E AbdelrahmanAbstract:Objective: To develop an effective and scalable individual-level patient cost prediction method by automatically learning hidden temporal patterns from Multivariate Time Series data in patient insurance claims using a convolutional neural network (CNN) architecture. Methods: We used three years of medical and pharmacy claims data from 2013 to 2016 from a healthcare insurer, where data from the first two years were used to build the model to predict costs in the third year. The data consisted of the Multivariate Time Series of cost, visit and medical features that were shaped as images of patients' health status (i.e., matrices with Time windows on one dimension and the medical, visit and cost features on the other dimension). Patients' Multivariate Time Series images were given to a CNN method with a proposed architecture. After hyper-parameter tuning, the proposed architecture consisted of three building blocks of convolution and pooling layers with an LReLU activation function and a customized kernel size at each layer for healthcare data. The proposed CNN learned temporal patterns became inputs to a fully connected layer. Conclusions: Feature learning through the proposed CNN configuration significantly improved individual-level healthcare cost prediction. The proposed CNN was able to outperform temporal pattern detection methods that look for a pre-defined set of pattern shapes, since it is capable of extracting a variable number of patterns with various shapes. Temporal patterns learned from medical, visit and cost data made significant contributions to the prediction performance. Hyper-parameter tuning showed that considering three-month data patterns has the highest prediction accuracy. Our results showed that patients' images extracted from Multivariate Time Series data are different from regular images, and hence require unique designs of CNN architectures.
Qi Zhang - One of the best experts on this subject based on the ideXlab platform.
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spectral temporal graph neural network for Multivariate Time Series forecasting
arXiv: Learning, 2021Co-Authors: Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Congrui Huang, Yunhai Tong, Jing Bai, Jie Tong, Qi ZhangAbstract:Multivariate Time-Series forecasting plays a crucial role in many real-world applications. It is a challenging problem as one needs to consider both intra-Series temporal correlations and inter-Series correlations simultaneously. Recently, there have been multiple works trying to capture both correlations, but most, if not all of them only capture temporal correlations in the Time domain and resort to pre-defined priors as inter-Series relationships. In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of Multivariate Time-Series forecasting. StemGNN captures inter-Series correlations and temporal dependencies \textit{jointly} in the \textit{spectral domain}. It combines Graph Fourier Transform (GFT) which models inter-Series correlations and Discrete Fourier Transform (DFT) which models temporal dependencies in an end-to-end framework. After passing through GFT and DFT, the spectral representations hold clear patterns and can be predicted effectively by convolution and sequential learning modules. Moreover, StemGNN learns inter-Series correlations automatically from the data without using pre-defined priors. We conduct extensive experiments on ten real-world datasets to demonstrate the effectiveness of StemGNN. Code is available at this https URL
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spectral temporal graph neural network for Multivariate Time Series forecasting
Neural Information Processing Systems, 2020Co-Authors: Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Congrui Huang, Yunhai Tong, Jing Bai, Jie Tong, Qi ZhangAbstract:Multivariate Time-Series forecasting plays a crucial role in many real-world applications. It is a challenging problem as one needs to consider both intra-Series temporal correlations and inter-Series correlations simultaneously. Recently, there have been multiple works trying to capture both correlations, but most, if not all of them only capture temporal correlations in the Time domain and resort to pre-defined priors as inter-Series relationships. In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of Multivariate Time-Series forecasting. StemGNN captures inter-Series correlations and temporal dependencies jointly in the spectral domain. It combines Graph Fourier Transform (GFT) which models inter-Series correlations and Discrete Fourier Transform (DFT) which models temporal dependencies in an end-to-end framework. After passing through GFT and DFT, the spectral representations hold clear patterns and can be predicted effectively by convolution and sequential learning modules. Moreover, StemGNN learns inter-Series correlations automatically from the data without using pre-defined priors. We conduct extensive experiments on ten real-world datasets to demonstrate the effectiveness of StemGNN.
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Multivariate Time Series anomaly detection via graph attention network
arXiv: Learning, 2020Co-Authors: Hang Zhao, Defu Cao, Yujing Wang, Juanyong Duan, Congrui Huang, Yunhai Tong, Jing Bai, Jie Tong, Qi ZhangAbstract:Anomaly detection on Multivariate Time-Series is of great importance in both data mining research and industrial applications. Recent approaches have achieved significant progress in this topic, but there is remaining limitations. One major limitation is that they do not capture the relationships between different Time-Series explicitly, resulting in inevitable false alarms. In this paper, we propose a novel self-supervised framework for Multivariate Time-Series anomaly detection to address this issue. Our framework considers each univariate Time-Series as an individual feature and includes two graph attention layers in parallel to learn the complex dependencies of Multivariate Time-Series in both temporal and feature dimensions. In addition, our approach jointly optimizes a forecasting-based model and are construction-based model, obtaining better Time-Series representations through a combination of single-Timestamp prediction and reconstruction of the entire Time-Series. We demonstrate the efficacy of our model through extensive experiments. The proposed method outperforms other state-of-the-art models on three real-world datasets. Further analysis shows that our method has good interpretability and is useful for anomaly diagnosis.
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Multivariate Time-Series Anomaly Detection via Graph Attention Network
2020Co-Authors: Zhao Hang, Cao Defu, Wang Yujing, Duan Juanyong, Tong Yunhai, Xu Bixiong, Bai Jing, Tong Jie, Huang Congrui, Qi ZhangAbstract:Anomaly detection on Multivariate Time-Series is of great importance in both data mining research and industrial applications. Recent approaches have achieved significant progress in this topic, but there is remaining limitations. One major limitation is that they do not capture the relationships between different Time-Series explicitly, resulting in inevitable false alarms. In this paper, we propose a novel self-supervised framework for Multivariate Time-Series anomaly detection to address this issue. Our framework considers each univariate Time-Series as an individual feature and includes two graph attention layers in parallel to learn the complex dependencies of Multivariate Time-Series in both temporal and feature dimensions. In addition, our approach jointly optimizes a forecasting-based model and are construction-based model, obtaining better Time-Series representations through a combination of single-Timestamp prediction and reconstruction of the entire Time-Series. We demonstrate the efficacy of our model through extensive experiments. The proposed method outperforms other state-of-the-art models on three real-world datasets. Further analysis shows that our method has good interpretability and is useful for anomaly diagnosis.Comment: Accepted by ICDM 2020. 10 page
Defu Cao - One of the best experts on this subject based on the ideXlab platform.
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spectral temporal graph neural network for Multivariate Time Series forecasting
arXiv: Learning, 2021Co-Authors: Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Congrui Huang, Yunhai Tong, Jing Bai, Jie Tong, Qi ZhangAbstract:Multivariate Time-Series forecasting plays a crucial role in many real-world applications. It is a challenging problem as one needs to consider both intra-Series temporal correlations and inter-Series correlations simultaneously. Recently, there have been multiple works trying to capture both correlations, but most, if not all of them only capture temporal correlations in the Time domain and resort to pre-defined priors as inter-Series relationships. In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of Multivariate Time-Series forecasting. StemGNN captures inter-Series correlations and temporal dependencies \textit{jointly} in the \textit{spectral domain}. It combines Graph Fourier Transform (GFT) which models inter-Series correlations and Discrete Fourier Transform (DFT) which models temporal dependencies in an end-to-end framework. After passing through GFT and DFT, the spectral representations hold clear patterns and can be predicted effectively by convolution and sequential learning modules. Moreover, StemGNN learns inter-Series correlations automatically from the data without using pre-defined priors. We conduct extensive experiments on ten real-world datasets to demonstrate the effectiveness of StemGNN. Code is available at this https URL
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spectral temporal graph neural network for Multivariate Time Series forecasting
Neural Information Processing Systems, 2020Co-Authors: Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Congrui Huang, Yunhai Tong, Jing Bai, Jie Tong, Qi ZhangAbstract:Multivariate Time-Series forecasting plays a crucial role in many real-world applications. It is a challenging problem as one needs to consider both intra-Series temporal correlations and inter-Series correlations simultaneously. Recently, there have been multiple works trying to capture both correlations, but most, if not all of them only capture temporal correlations in the Time domain and resort to pre-defined priors as inter-Series relationships. In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of Multivariate Time-Series forecasting. StemGNN captures inter-Series correlations and temporal dependencies jointly in the spectral domain. It combines Graph Fourier Transform (GFT) which models inter-Series correlations and Discrete Fourier Transform (DFT) which models temporal dependencies in an end-to-end framework. After passing through GFT and DFT, the spectral representations hold clear patterns and can be predicted effectively by convolution and sequential learning modules. Moreover, StemGNN learns inter-Series correlations automatically from the data without using pre-defined priors. We conduct extensive experiments on ten real-world datasets to demonstrate the effectiveness of StemGNN.
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Multivariate Time Series anomaly detection via graph attention network
arXiv: Learning, 2020Co-Authors: Hang Zhao, Defu Cao, Yujing Wang, Juanyong Duan, Congrui Huang, Yunhai Tong, Jing Bai, Jie Tong, Qi ZhangAbstract:Anomaly detection on Multivariate Time-Series is of great importance in both data mining research and industrial applications. Recent approaches have achieved significant progress in this topic, but there is remaining limitations. One major limitation is that they do not capture the relationships between different Time-Series explicitly, resulting in inevitable false alarms. In this paper, we propose a novel self-supervised framework for Multivariate Time-Series anomaly detection to address this issue. Our framework considers each univariate Time-Series as an individual feature and includes two graph attention layers in parallel to learn the complex dependencies of Multivariate Time-Series in both temporal and feature dimensions. In addition, our approach jointly optimizes a forecasting-based model and are construction-based model, obtaining better Time-Series representations through a combination of single-Timestamp prediction and reconstruction of the entire Time-Series. We demonstrate the efficacy of our model through extensive experiments. The proposed method outperforms other state-of-the-art models on three real-world datasets. Further analysis shows that our method has good interpretability and is useful for anomaly diagnosis.
Min Han - One of the best experts on this subject based on the ideXlab platform.
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Improved extreme learning machine for Multivariate Time Series online sequential prediction
Engineering Applications of Artificial Intelligence, 2015Co-Authors: Xinying Wang, Min HanAbstract:Multivariate Time Series has attracted increasing attention due to its rich dynamic information of the underlying systems. This paper presents an improved extreme learning machine for online sequential prediction of Multivariate Time Series. The Multivariate Time Series is first phase-space reconstructed to form the input and output samples. Extreme learning machine, which has simple structure and good performance, is used as prediction model. On the basis of the specific network function of extreme learning machine, an improved Levenberg-Marquardt algorithm, in which Hessian matrix and gradient vector are calculated iteratively, is developed to implement online sequential prediction. Finally, simulation results of artificial and real-world Multivariate Time Series are provided to substantiate the effectiveness of the proposed method.
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joint mutual information based input variable selection for Multivariate Time Series modeling
Engineering Applications of Artificial Intelligence, 2015Co-Authors: Min Han, Weijie Ren, Xiaoxin LiuAbstract:Abstract For modeling of Multivariate Time Series, input variable selection is a key problem. This paper presents the estimation of joint mutual information and its application in input variable selection problems. Mutual information is a commonly used measure for variable selection. To improve the performance of input variable selection, we propose a novel high-dimensional mutual information estimator based on copula entropy, which is estimated by the truncated k -nearest neighbor method. Simulations on high dimensional Gaussian distributions substantiate the effectiveness of the proposed mutual information estimator. A relationship between the joint mutual information and the copula entropy is derived, which is used for joint mutual information estimation. Then the proposed estimator is applied to input variable selection for Multivariate Time Series modeling based on the criterion of max dependency and max–min dependency. A stop criterion is proposed to terminate the selection process automatically. Simulation results show that the input variable selection method works well on both synthetic and real life dataset.
Mohammad Amin Morid - One of the best experts on this subject based on the ideXlab platform.
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learning hidden patterns from patient Multivariate Time Series data using convolutional neural networks a case study of healthcare cost prediction
Journal of Biomedical Informatics, 2020Co-Authors: Mohammad Amin Morid, Olivia Liu R Sheng, Kensaku Kawamoto, Samir E AbdelrahmanAbstract:Abstract Objective To develop an effective and scalable individual-level patient cost prediction method by automatically learning hidden temporal patterns from Multivariate Time Series data in patient insurance claims using a convolutional neural network (CNN) architecture. Methods We used three years of medical and pharmacy claims data from 2013 to 2016 from a healthcare insurer, where data from the first two years were used to build the model to predict costs in the third year. The data consisted of the Multivariate Time Series of cost, visit and medical features that were shaped as images of patients’ health status (i.e., matrices with Time windows on one dimension and the medical, visit and cost features on the other dimension). Patients’ Multivariate Time Series images were given to a CNN method with a proposed architecture. After hyper-parameter tuning, the proposed architecture consisted of three building blocks of convolution and pooling layers with an LReLU activation function and a customized kernel size at each layer for healthcare data. The proposed CNN learned temporal patterns became inputs to a fully connected layer. We benchmarked the proposed method against three other methods: (1) a spike temporal pattern detection method, as the most accurate method for healthcare cost prediction described to date in the literature; (2) a symbolic temporal pattern detection method, as the most common approach for leveraging healthcare temporal data; and (3) the most commonly used CNN architectures for image pattern detection (i.e., AlexNet, VGGNet and ResNet) (via transfer learning). Moreover, we assessed the contribution of each type of data (i.e., cost, visit and medical). Finally, we externally validated the proposed method against a separate cohort of patients. All prediction performances were measured in terms of mean absolute percentage error (MAPE). Results The proposed CNN configuration outperformed the spike temporal pattern detection and symbolic temporal pattern detection methods with a MAPE of 1.67 versus 2.02 and 3.66, respectively (p Conclusions Feature learning through the proposed CNN configuration significantly improved individual-level healthcare cost prediction. The proposed CNN was able to outperform temporal pattern detection methods that look for a pre-defined set of pattern shapes, since it is capable of extracting a variable number of patterns with various shapes. Temporal patterns learned from medical, visit and cost data made significant contributions to the prediction performance. Hyper-parameter tuning showed that considering three-month data patterns has the highest prediction accuracy. Our results showed that patients’ images extracted from Multivariate Time Series data are different from regular images, and hence require unique designs of CNN architectures. The proposed method for converting Multivariate Time Series data of patients into images and tuning them for convolutional learning could be applied in many other healthcare applications with Multivariate Time Series data.
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learning hidden patterns from patient Multivariate Time Series data using convolutional neural networks a case study of healthcare cost prediction
arXiv: Learning, 2020Co-Authors: Mohammad Amin Morid, Olivia Liu R Sheng, Kensaku Kawamoto, Samir E AbdelrahmanAbstract:Objective: To develop an effective and scalable individual-level patient cost prediction method by automatically learning hidden temporal patterns from Multivariate Time Series data in patient insurance claims using a convolutional neural network (CNN) architecture. Methods: We used three years of medical and pharmacy claims data from 2013 to 2016 from a healthcare insurer, where data from the first two years were used to build the model to predict costs in the third year. The data consisted of the Multivariate Time Series of cost, visit and medical features that were shaped as images of patients' health status (i.e., matrices with Time windows on one dimension and the medical, visit and cost features on the other dimension). Patients' Multivariate Time Series images were given to a CNN method with a proposed architecture. After hyper-parameter tuning, the proposed architecture consisted of three building blocks of convolution and pooling layers with an LReLU activation function and a customized kernel size at each layer for healthcare data. The proposed CNN learned temporal patterns became inputs to a fully connected layer. Conclusions: Feature learning through the proposed CNN configuration significantly improved individual-level healthcare cost prediction. The proposed CNN was able to outperform temporal pattern detection methods that look for a pre-defined set of pattern shapes, since it is capable of extracting a variable number of patterns with various shapes. Temporal patterns learned from medical, visit and cost data made significant contributions to the prediction performance. Hyper-parameter tuning showed that considering three-month data patterns has the highest prediction accuracy. Our results showed that patients' images extracted from Multivariate Time Series data are different from regular images, and hence require unique designs of CNN architectures.
