Intelligent infusion controller with a physiological information feedback function

Li, Jing; Pengfei, Dong; Yongxin, Lai; Qin, Xin; Bai, Jing; Wang, Hongbo; Wan, Z.G.; Ji, Zhenyu; Shi, Xuetao

doi:10.3233/thc-209005

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…Design of portable lightweight infusion pump with miniaturized components with battery powered motors are suitable to treat patients in clinical and home environment. Recent studies on decision making of the infusion pump speed based on the flow of the fluid using Fuzzy logic techniques [19]. The infusion pump control parameters such as dead volume, lag time, prediction accuracy, optimal speed control of the motor are essential to infuse the required volume of the drug and fluid.…”

Section: Machine Learning Methods To Investigate Drug Delivery In Infusion Pumpmentioning

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: Machine Learning Methods To Investigate Drug Delivery In Infusion Pumpmentioning

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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“…According to the current literature, accuracy, precision, and setup time are crucial considerations when comparing medical devices (5). In low-resource environments, such as India and Senegal, both overall costs and running considerations, such as electricity reliance, should be considered (13)(14)(15)(16)(17). The perceptions of healthcare workers are additionally important when it comes to the use of devices (18,19).…”

Section: Introductionmentioning

confidence: 99%

A comparative analysis of intravenous infusion methods for low-resource environments

Tomobi,

Avoian,

Ekwere

et al. 2024

Front. Med.

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IntroductionIntravenous (IV) therapy is a crucial aspect of care for the critically ill patient. Barriers to IV infusion pumps in low-resource settings include high costs, lack of access to electricity, and insufficient technical support. Inaccuracy of traditional drop-counting practices places patients at risk. By conducting a comparative assessment of IV infusion methods, we analyzed the efficacy of different devices and identified one that most effectively bridges the gap between accuracy, cost, and electricity reliance in low-resource environments.MethodsIn this prospective mixed methods study, nurses, residents, and medical students used drop counting, a manual flow regulator, an infusion pump, a DripAssist, and a DripAssist with manual flow regulator to collect normal saline at goal rates of 240, 120, and 60 mL/h. Participants’ station setup time was recorded, and the amount of fluid collected in 10 min was recorded (in milliliters). Participants then filled out a post-trial survey to rate each method (on a scale of 1 to 5) in terms of understandability, time consumption, and operability. Cost-effectiveness for use in low-resource settings was also evaluated.ResultsThe manual flow regulator had the fastest setup time, was the most cost effective, and was rated as the least time consuming to use and the easiest to understand and operate. In contrast, the combination of the DripAssist and manual flow regulator was the most time consuming to use and the hardest to understand and operate.ConclusionThe manual flow regulator alone was the least time consuming and easiest to operate. The DripAssist/Manual flow regulator combination increases accuracy, but this combination was the most difficult to operate. In addition, the manual flow regulator was the most cost-effective. Healthcare providers can adapt these devices to their practice environments and improve the safety of rate-sensitive IV medications without significant strain on electricity, time, or personnel resources.

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Intelligent infusion controller with a physiological information feedback function

Cited by 2 publications

References 9 publications

Machine Learning Methods to Investigate Drug Delivery in Infusion Pump

Machine Learning Methods to Investigate Drug Delivery in Infusion Pump

A comparative analysis of intravenous infusion methods for low-resource environments

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