Forecasting emergency trolley drug utilisation using ARMA and SVR with external factors

Adequate stock management in emergency trolleys is important to ensure that every process requiring medication specifically in hospitals, runs smoothly in any given situation. Stock management based on the value of means (or average usage) is not adequate to account for unpredictable situations that...

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Main Author:	Norrulashikin, Muhammad Adam
Format:	Thesis
Language:	English
Published:	2022
Subjects:	QC Physics
Online Access:	http://eprints.utm.my/102990/1/MuhammadAdamNorrulashikinMFS2022.pdf.pdf
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spelling	my-utm-ep.1029902023-10-12T08:39:19Z Forecasting emergency trolley drug utilisation using ARMA and SVR with external factors 2022 Norrulashikin, Muhammad Adam QC Physics Adequate stock management in emergency trolleys is important to ensure that every process requiring medication specifically in hospitals, runs smoothly in any given situation. Stock management based on the value of means (or average usage) is not adequate to account for unpredictable situations that may result in disruptions in drug utilisation and supply. In this study, an investigation to identify possible factors that correlate with the fluctuation of terbutaline injection drug utilisation used in emergency trolleys, using univariate forecasting methods, machine learning (ML), and hybrid models capable of predicting future usage was undertaken. Based on an experimental dataset, it was found that the mean temperature in Mersing, Johor, has the highest negative correlation with terbutaline injection utilisation, at a correlation coefficient value of -0.27 (p-value = 0.0068). Three univariate models and three univariate models with exogenous variables were constructed and compared. All advanced models show better performance than the naive baseline model that served as a benchmark for model building. The univariate models in the analysis consisted of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) models. Also, the ML models considered in this study including Support Vector Regression (SVR), used the lagged values of terbutaline injection as its input variables. The Autoregressive Moving Average (ARMA) (4,4) model performed better than the ML models, with a Mean Absolute Error (MAE) of 8.9524 and a Root Mean Square Error (RMSE) of 11.4518 in the validation data set. To incorporate the effects of exogenous variables, significant lags of emergency admission and climate variables were used to construct a predictive model from ARMA (4,4). The hybrid model of ARMA-ANN outperformed all other models, with MAE and RMSE values of 8.8571 and 10.8496 respectively. It can also be summarized that models utilising ANN are far better than SVR models due to a variety of factors, including the type of data input and the optimization techniques used to build the SVR models. Future studies should focus on modelling different types of medication used in emergency trolleys. The application of other ML algorithms and optimisation strategies can also be explored for different patterns of data. 2022 Thesis http://eprints.utm.my/102990/ http://eprints.utm.my/102990/1/MuhammadAdamNorrulashikinMFS2022.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:150754 masters Universiti Teknologi Malaysia Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QC Physics
spellingShingle	QC Physics Norrulashikin, Muhammad Adam Forecasting emergency trolley drug utilisation using ARMA and SVR with external factors
description	Adequate stock management in emergency trolleys is important to ensure that every process requiring medication specifically in hospitals, runs smoothly in any given situation. Stock management based on the value of means (or average usage) is not adequate to account for unpredictable situations that may result in disruptions in drug utilisation and supply. In this study, an investigation to identify possible factors that correlate with the fluctuation of terbutaline injection drug utilisation used in emergency trolleys, using univariate forecasting methods, machine learning (ML), and hybrid models capable of predicting future usage was undertaken. Based on an experimental dataset, it was found that the mean temperature in Mersing, Johor, has the highest negative correlation with terbutaline injection utilisation, at a correlation coefficient value of -0.27 (p-value = 0.0068). Three univariate models and three univariate models with exogenous variables were constructed and compared. All advanced models show better performance than the naive baseline model that served as a benchmark for model building. The univariate models in the analysis consisted of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) models. Also, the ML models considered in this study including Support Vector Regression (SVR), used the lagged values of terbutaline injection as its input variables. The Autoregressive Moving Average (ARMA) (4,4) model performed better than the ML models, with a Mean Absolute Error (MAE) of 8.9524 and a Root Mean Square Error (RMSE) of 11.4518 in the validation data set. To incorporate the effects of exogenous variables, significant lags of emergency admission and climate variables were used to construct a predictive model from ARMA (4,4). The hybrid model of ARMA-ANN outperformed all other models, with MAE and RMSE values of 8.8571 and 10.8496 respectively. It can also be summarized that models utilising ANN are far better than SVR models due to a variety of factors, including the type of data input and the optimization techniques used to build the SVR models. Future studies should focus on modelling different types of medication used in emergency trolleys. The application of other ML algorithms and optimisation strategies can also be explored for different patterns of data.
format	Thesis
qualification_level	Master's degree
author	Norrulashikin, Muhammad Adam
author_facet	Norrulashikin, Muhammad Adam
author_sort	Norrulashikin, Muhammad Adam
title	Forecasting emergency trolley drug utilisation using ARMA and SVR with external factors
title_short	Forecasting emergency trolley drug utilisation using ARMA and SVR with external factors
title_full	Forecasting emergency trolley drug utilisation using ARMA and SVR with external factors
title_fullStr	Forecasting emergency trolley drug utilisation using ARMA and SVR with external factors
title_full_unstemmed	Forecasting emergency trolley drug utilisation using ARMA and SVR with external factors
title_sort	forecasting emergency trolley drug utilisation using arma and svr with external factors
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Science
publishDate	2022
url	http://eprints.utm.my/102990/1/MuhammadAdamNorrulashikinMFS2022.pdf.pdf
_version_	1783729232781770752

Forecasting emergency trolley drug utilisation using ARMA and SVR with external factors

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