Undertaking drug calculations for intravenous medicines and infusions

Brindley, Jane

doi:10.7748/ns.2018.e11029

Cited by 10 publications

(5 citation statements)

References 8 publications

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“…The key attributes of infusion flow rate (Qd ) are drug dosage, drip rate, carrier flow (Qc). The total flow rate (Qt) is calculated as, Qt = Qd + Qc [28]. For an infusion pump, its flow rate is based on the speed of the motor () and it is related as, Qt = k x  where k is the proportionality constant.…”

Section: A Infusion Pump Parametersmentioning

confidence: 99%

Machine Learning Methods to Investigate Drug Delivery in Infusion Pump

Alamelu

Mythili

2022

International Journal of Biology and Biomedical Engineering

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In the current scenario, usage of the smart medical pump is predominant in the medical field. The precise drug dosage, flow accuracy should be maintained to increase the performance of an infusion pump. In this work, an attempt has been made to predict and control the speed of the infusion pump for suitable infusion flowrate using machine learning technique and Linear Quadratic Gaussian (LQG) controller. The data for this study is considered from the publicly available online database, electronic Medicines Compendium (eMC). The speed of the infusion pump has been calculated using the drug dosage and flow rate for two different drugs. The prediction of infusion pump speed is achieved using Linear regression with Principal Component analysis (PCR) and Support Vector Machine Regression (SVR). The performance of the prediction schemes is evaluated using standard metrics. To validate the optimal control of the predicted speed, two different medical graded motors are considered. Further, the optimal control of the pump speed is investigated using Proportional–Integral–Derivative (PID), Linear Quadratic Regulator (LQR), and LQG controllers for its stability criteria. The prediction of the pump speed using regression models PCR, SVR has been verified and then the transient response analysis with rise time, settling time for both the motors have been examined. Results demonstrate that the LQG optimal control strategy achieves fast rise time, settling time of motor1 with 0.653s, 1.15s, and 0.22, 0.392s for motor2 respectively.

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Section: A Infusion Pump Parametersmentioning

confidence: 99%

Machine Learning Methods to Investigate Drug Delivery in Infusion Pump

Alamelu

Mythili

2022

International Journal of Biology and Biomedical Engineering

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“…160 data instances of drug dosage and flow rate for mean patient weight of 60 kg have been retrieved from emc. (21) As per the nursing information, the drug dosage was measured in terms of mcg/kg/min. Furthermore, based on the nursing calculations, the dosage can be converted to the infusion flow rate in ml/hr.…”

Section: Infusion Ratementioning

confidence: 99%

Machine learning approach to predict delay in smart infusion pump

Alamelu

Sivanantham

2022

Salud, Ciencia y Tecnología

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Wireless smart infusion pumps are currently under development. It is critical to ensure that the patient receives the correct drug concentration. Practically, the performance of the pump has relied on the minimum startup delay. The minimization of the startup delay is prominent in open-type infusion pumps and rarely in closed types. The emphasis on reducing startup delay puts practitioners and caregivers at ease while ensuring patient safety. The startup delay of the infusion pump is based on the flow rate and the lag time. The prediction of the flow rate and lag time for an infusion pump is necessitated to ensure a safe drug dosage for the patient. Currently, machine learning methods and computational methods to predict the desired parameter are widely used in healthcare applications and medical device performance. The reduction of start-up delay can be achieved by predicting its associated parameters lag time and flow rate. The flow rate is dependent on the speed of the infusion pump, which has to be calculated based on the number of gears and revolutions. The speed of the pump has to be predicted for accurate flow delivery. Our present research attempts to predict the lag time of an infusion pump using different kernel functions of support vector regression (SVR). The performance of the SVR for each kernel function is compared with R2, RMSE, MAE, and prediction accuracy. The prediction accuracy of 99,7 % has been obtained in optimized SVM.

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“…The pump controls the number of drops per time (drip rate in drops/min) and the infusion velocity. The volume depends on the drop's size, varies with the type of equipment, temperature, liquid viscosity, and density [30]. The parameters and the control strategy used in infusion pumps are listed in Figure 2 below.…”

Section: Control System Strategy For Infusion Pumpmentioning

confidence: 99%

Review of Medical Cyber Physical System based Infusion Pump-perception on control strategy

Alamelu¹,

Mythili²

2018

EAI Endorsed Transactions on Energy Web

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The revolutionary change in recent technologies has dominated the Medical field. Huge medical devices are deployed in hospitals and in-home environment. The innovations of smart medical devices are prominent which can be used in the fully automated and semi-automated environment. For several decades, numerous medical devices are designed and utilized effectively. These devices are designed for the past few years, manufactured in micro and nanotechnologies with smart sensing techniques. Such devices are network-enabled and integrated into the cyber world in physical means, leading to a medical cyber physical system. The design, working, and optimal control of smart medical devices are essential. In this paper, the emphasis on the review of infusion pump control mechanism with essential parameters such as drug delivery, flow rate, pump control has been considered.

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Undertaking drug calculations for intravenous medicines and infusions

Cited by 10 publications

References 8 publications

Machine Learning Methods to Investigate Drug Delivery in Infusion Pump

Machine Learning Methods to Investigate Drug Delivery in Infusion Pump

Machine learning approach to predict delay in smart infusion pump

Review of Medical Cyber Physical System based Infusion Pump-perception on control strategy

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