Feature selection based multivariate time series forecasting: An application to antibiotic resistance outbreaks prediction

Jiménez, Fernando; Palma, José; Sánchez, Gracia; Marín, David Hervás; Palacios, Martha; Lopez, Myriam

doi:10.1016/j.artmed.2020.101818

Cited by 39 publications

(23 citation statements)

References 54 publications

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“…These types of evaluation measures depend on the problem type: regression or classification. For instance, the Mean Absolute Error (MAE) [13] , [18] , [22] , Mean Absolute Percentage Error (MAPE) [17] , [19] , [23] , [26] , [38] , [39] , [42] , Root-Mean-Square Error (RMSE) [25] , [28] , [30] , [31] , [32] , [33] , [40] and Mean Squared Error (MSE) [15] , [20] , [20] , [27] , [41] evaluation measures are used to solve the regression problems and Accuracy [22] , [49] , [53] , [54] , [55] , [56] , F 1 Score [43] , [46] , [47] , AUC-ROC [44] , [48] , [51] evaluation measures are used to solve the classification problems [67] . The performance comparison of various approaches and metrics are discussed in more detail in Section 6.4 .…”

Section: Contents Summarizationmentioning

confidence: 99%

“…A feature selection based Time Series forecasting has been proposed for predicting future outbreaks of Methicilin-resistant Staphylococcus aereus (MRSA) [18] . The performance of the feature selection methods has been measured using the root mean square error (RMSE) and mean absolute error (MAE) performance metrics with RMSE and MAR values of 0.1349 and 0.1003 respectively.…”

Section: Reporting Of Review Findingsmentioning

confidence: 99%

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The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review

Alfred¹,

Obit²

2021

Heliyon

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Machine learning (ML) methods can be leveraged to prevent the spread of deadly infectious disease outbreak (e.g., COVID-19). This can be done by applying machine learning methods in predicting and detecting the deadly infectious disease. Most reviews did not discuss about the machine learning algorithms, datasets and performance measurements used for various applications in predicting and detecting the deadly infectious disease. In contrast, this paper outlines the literature review based on two major ways (e.g., prediction, detection) to limit the spread of deadly disease outbreaks. Hence, this study aims to investigate the state of the art, challenges and future works of leveraging ML methods to detect and predict deadly disease outbreaks according to two categories mentioned earlier. Specifically, this study provides a review on various approaches (e.g., individual and ensemble models), types of datasets, parameters or variables and performance measures used in the previous works. The literature review included all articles from journals and conference proceedings published from 2010 through 2020 in Scopus indexed databases using the search terms Predicting Disease Outbreaks and/or Detecting Disease using Machine Learning . The findings from this review focus on commonly used machine learning approaches, challenges and future works to limit the spread of deadly disease outbreaks through preventions and detections.

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Section: Contents Summarizationmentioning

confidence: 99%

Section: Reporting Of Review Findingsmentioning

confidence: 99%

The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review

Alfred¹,

Obit²

2021

Heliyon

View full text Add to dashboard Cite

show abstract

“…The authors used Google trend for forecasting. Other health areas such as antibiotic resistance outbreaks [48] and influenza outbreaks [49,50] utilized multivariate regression models. Different algorithms such as deep neural network [51,52], long short-term memory model (LSTM) [53] and gated recurrent unit (GRU)-based model [54] have been successfully applied in various forecasts.…”

Section: Related Workmentioning

confidence: 99%

Neural Network Based Country Wise Risk Prediction of COVID-19

et al. 2020

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The recent worldwide outbreak of the novel coronavirus (COVID-19) has opened up new challenges to the research community. Artificial intelligence (AI) driven methods can be useful to predict the parameters, risks, and effects of such an epidemic. Such predictions can be helpful to control and prevent the spread of such diseases. The main challenges of applying AI is the small volume of data and the uncertain nature. Here, we propose a shallow long short-term memory (LSTM) based neural network to predict the risk category of a country. We have used a Bayesian optimization framework to optimize and automatically design country-specific networks. The results show that the proposed pipeline outperforms state-of-the-art methods for data of 180 countries and can be a useful tool for such risk categorization. We have also experimented with the trend data and weather data combined for the prediction. The outcome shows that the weather does not have a significant role. The tool can be used to predict long-duration outbreak of such an epidemic such that we can take preventive steps earlier.

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“…The method uses a feature attention mechanisms to predict future health risks. Other health areas such as antibiotic resistance outbreaks [41] and influenza outbreaks [42,43] are also used multivariate regression models. Different algorithms such as deep neural network [44,45], long short-term memory model (LSTM) [46], and gated recurrent Unit(GRU)-based model [47] are successfully applied in various forecasting.…”

Section: (C) Multivariate Regression In Aimentioning

confidence: 99%

Neural Network Based Country Wise Risk Prediction of COVID-19

Pal¹,

Sekh²,

Kar³

et al. 2020

Preprint

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The recent worldwide outbreak of the novel corona-virus (COVID-19) opened up new challenges to the research community. Artificial intelligence (AI) driven methods can be useful to predict the parameters, risks, and effects of such an epidemic. Such predictions can be helpful to control and prevent the spread of such diseases. The main challenges of applying AI is the small volume of data and the uncertain nature. Here, we propose a shallow Long short-term memory (LSTM) based neural network to predict the risk category of a country. We have used a Bayesian optimization framework to optimized and automatically design country-specific networks. We have combined the trend data and weather data together for the prediction. The results show that the proposed pipeline outperforms against state-of-the-art methods for 170 countries data and can be a useful tool for such risk categorization. The tool can be used to predict long-duration outbreak of such an epidemic such that we can take preventive steps earlier.

show abstract

Feature selection based multivariate time series forecasting: An application to antibiotic resistance outbreaks prediction

Cited by 39 publications

References 54 publications

The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review

The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review

Neural Network Based Country Wise Risk Prediction of COVID-19

Neural Network Based Country Wise Risk Prediction of COVID-19

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