Time series classification for the prediction of dialysis in critically ill patients using echo statenetworks

Ongenae, Femke; Looy, Stijn Van; Verstraeten, David; Verplancke, T; Benoît, Dominique; Turck, Filip De; Dhaene, Tom; Schrauwen, Benjamin; Decruyenaere, Johan

doi:10.1016/j.engappai.2012.09.019

Cited by 18 publications

(10 citation statements)

References 32 publications

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“…The minimum of the criterion function is found by changing the values of the weights matrix W out , which is determined by Eqs. (5), (6), (9) and (10). The basic echo state neural networks model consists of one input layer within 96 neurons, (SI1(t), .…”

Section: Echo State Neural Network Mod-ellingmentioning

confidence: 99%

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A 24h forecast of solar irradiance using echo state neural networks

Kmeť

Kmetova

2015

Proceedings of the 16th International Conference on Engineering Applications of Neural Networks (INNS)

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Artificial neural networks have demonstrated to be good at timeseries forecasting problems, being widely studied in literature. In this study an artificial neural network model is introduced for modelling the solar irradiance. Forecasting of solar irradiance is in general significant for planning the operations of power plants which convert renewable energies into electricity. In particular, the possibility to predict the solar irradiance (up to 24h or even more) can become fundamental in making power dispatching plans. In this paper, a practical method for solar irradiance forecast using artificial neural network is presented. The proposed echo state neural networks model makes it possible to forecast the solar irradiance on the base of 24h using the present values of the mean hours solar irradiance, air temperature, humidity, and the wind speed. An experimental database of solar irradiance, air temperature, humidity, wind speed data (from October 17th 2013 to May 11th 2015) has been used. The database has been collected in Lucenec (48.33N 19.67E), Slovakia.The results indicate that the proposed model performs well, while the correlation coefficient between measured and forecasted solar irradiance is in the range 0.94 − 0.97.

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Section: Echo State Neural Network Mod-ellingmentioning

confidence: 99%

“…is defined as follows: The key idea in reservoir computing [10], [14] is to feed time series to a reservoir by modelling the dynamics of the system which generates the time series. The reservoir is then read by a readout function in order to make predictions using the constructed model.…”

Section: Echo State Neural Networkmentioning

confidence: 99%

A 24h forecast of solar irradiance using echo state neural networks

Kmeť

Kmetova

2015

Proceedings of the 16th International Conference on Engineering Applications of Neural Networks (INNS)

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“…The ESN application domains are various. They can be founded in medical [15], economic [16] and optical applications [17].…”

Section: A Backgrounds Of Esnmentioning

confidence: 99%

Fuel Cells prognostics using echo state network

Morando

Jemeï

Gouriveau

et al. 2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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One remaining technological bottleneck to develop industrial Fuel Cell (FC) applications resides in the system limited useful lifetime. Consequently, it is important to develop failure diagnostic and prognostic tools enabling the optimization of the FC. Among all the existing prognostics approaches, datamining methods such as artificial neural networks aim at estimating the process' behavior without huge knowledge about the underlying physical phenomena. Nevertheless, this kind of approach needs huge learning dataset. Also, the deployment of such an approach can be long (trial and error method), which represents a real problem for industrial applications where realtime complying algorithms must be developed. According to this, the aim of this paper is to study the application of a reservoir computing tool (the Echo State Network) as a prognostics system enabling the estimation of the Remaining Useful Life of a Proton Exchange Membrane Fuel Cell. Developments emphasize on the prediction of the mean voltage cells of a degrading FC. Accuracy and time consumption of the approach are studied, as well as sensitivity of several parameters of the ESN. Results appear to be very promising.

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“…There are quite a wide range of machine learning algorithms. This includes Naive Bayes [25], Support Vector Machines [34], Support Vector Regression [40], Gradient Boosted Regression Tree [11], Factorisation Machine [35] and Multilayer Perceptrons. They can be applied to time series forecasting of blood pres-sure but they are not specifically designed to deal with temporal data.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Machine Learning Techniques in Forecasting of Blood Pressure Time Series Data

Masum

Chiverton

Liu

et al. 2019

Lecture Notes in Computer Science

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The aim of this paper is to investigate different machine learning based forecasting techniques for forecasting of blood pressure and heart rate. Forecasting of blood pressure could potentially help a clinician to take preventative steps even before dangerous medical situations occur. This paper examines forecasting blood pressure 30 minutes in advance. Univariate and multivariate forecast models are considered. Different forecast strategies are also considered. To compare different forecast strategies, LSTM and BI-LSTM machine learning algorithms were included. Then univariate and multivariate LSTM, BI-LSTM and CNN machine learning algorithms were compared using the two best forecasting strategies. Comparative analysis between forecasting strategies suggest that MIMO and DIRMO forecast strategies provide the best accuracy in forecasting physiological time series data. Results also appear to show that multivariate forecast models for blood pressure and heart rate are more reliable compared to blood pressure alone. Comparative analysis between MIMO and DIRMO forecasting strategies appear to show that DIRMO is more reliable for both univariate and multivariate cases. Results also appear to show that the forecast model that uses BI-LSTM with the DIRMO strategy is the best overall.

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Time series classification for the prediction of dialysis in critically ill patients using echo statenetworks

Cited by 18 publications

References 32 publications

A 24h forecast of solar irradiance using echo state neural networks

A 24h forecast of solar irradiance using echo state neural networks

Fuel Cells prognostics using echo state network

Investigation of Machine Learning Techniques in Forecasting of Blood Pressure Time Series Data

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