Decision-Oriented Multi-Outcome Modeling for Anesthesia Patients

Anesthesia drugs have impact on multiple outcomes of an anesthesia patient. Most typical outcomes include anesthesia depth, blood pressures, heart rates, etc. Traditional diagnosis and control in anesthesia focus on a one-drug-one-outcome scenario. This paper studies the problem of real-time modeling for monitoring, diagnosing, and predicting multiple outcomes of anesthesia patients. It is shown that consideration of multiple outcomes is necessary and beneficial for anesthesia managements. Due to limited real-time data, real-time modeling in multi-outcome modeling requires low-complexity model strucrtures. This paper introduces a method of decision-oriented modeling that significantly reduces the complexity of the problem. The method employs simplified and combined model functions in a Wiener structure to contain model complexity. The ideas of drug impact prediction and reachable sets are introduced for utility of the models in diagnosis, outcome prediction, and decision assistance. Clinical data are used to evaluate the effectiveness of the method

Hybrid Approach for Automatic Drug Dispensing and Control

Clinical scenario for critically ill patients involves large number of medical devices, such as, bed side monitors that provide vital information about the admitted patient. Critically ill patients need prompt and perfect decision, so that lifesaving drugs can be delivered at a proper time. Moreover suggestions/ recommendations of a super specialist doctor may also be required in hospitals. The main problem in hospitals is lack of specialist doctors, so, patients are not able to approach to them on time. Automatic drug delivery using microprocessor/ microcontroller has improved tremendously in recent years due to advancement in Information and Communication Technology (ICT). This paper is focused on various Drug Delivery System Interfaces used in emergency cases to reduce manual intervention. We are proposing a hybrid approach for drug dispensing, which can work as open or closed loop system. Another feature of this system is that it can be controlled from a remote location, so is to provide virtual presence of a doctor. The system will help the doctor monitor the patient as per the immediate requirement and deliver drug remotely to patient using smart phone

Reach Control on Simplices by Piecewise Affine Feedback

We study the reach control problem for affine systems on simplices, and the focus is on cases when it is known that the problem is not solvable by continuous state feedback. We examine from a geometric viewpoint the structural properties of the system which make continuous state feedbacks fail. This structure is encoded by so-called reach control indices, which are defined and developed in the paper. Based on these indices, we propose a subdivision algorithm and associated piecewise affine feedback. The method is shown to solve the reach control problem in all remaining cases, assuming it is solvable by open-loop controls

Closed-loop control of anesthesia : survey on actual trends, challenges and perspectives

Automation empowers self-sustainable adaptive processes and personalized services in many industries. The implementation of the integrated healthcare paradigm built on Health 4.0 is expected to transform any area in medicine due to the lightning-speed advances in control, robotics, artificial intelligence, sensors etc. The two objectives of this article, as addressed to different entities, are: i) to raise awareness throughout the anesthesiologists about the usefulness of integrating automation and data exchange in their clinical practice for providing increased attention to alarming situations, ii) to provide the actualized insights of drug-delivery research in order to create an opening horizon towards precision medicine with significantly improved human outcomes. This article presents a concise overview on the recent evolution of closed-loop anesthesia delivery control systems by means of control strategies, depth of anesthesia monitors, patient modelling, safety systems, and validation in clinical trials. For decades, anesthesia control has been in the midst of transformative changes, going from simple controllers to integrative strategies of two or more components, but not achieving yet the breakthrough of an integrated system. However, the scientific advances that happen at high speed need a modern review to identify the current technological gaps, societal implications, and implementation barriers. This article provides a good basis for control research in clinical anesthesia to endorse new challenges for intelligent systems towards individualized patient care. At this connection point of clinical and engineering frameworks through (semi-) automation, the following can be granted: patient safety, economical efficiency, and clinicians' efficacy

Monitoring, diagnosis, and control for advanced anesthesia management

Modern anesthesia management is a comprehensive and the most critical issue in medical care. During the past dacades, a large amount of research works have been focused on the problems of monitoring anesthesia depth, modeling the dynamics of anesthesia patient for the purpose of control, prediction, and diagnosis. Monitoring the anesthesia depth is not only for keeping the patient in adquate anesthesia level but also for preventing the patient from overdosing. Several EEG based indexes have been developed such as the BIS, and Entropy etc. for measuring depth. However, reports mentioned that those indexes in some cases fail in detecting the awareness of the the patient. In this research work, a new EEG based parameter, beta_2/theta-ratio, was introduced as a potential enhancement in measuring anesthesia depth. It was compared to the relative beta-ratio which had been commercially used in the BIS monitor and proved that the beta_2/theta-ratio has improved reliability and sensitivity in detecting the awareness than the beta-ratio does. Traditional modeling, diagnosis and control in anesthesia focus on a one-drug one-outcome scenario. In fact, Anesthesia drugs have impact on multiple outcomes of an anesthesia patient. Due to limited real-time data, real-time modeling in multi-outcome modeling requires low complexity model structures. A method of decision-oriented modeling which employs simplified and combined model functions in a Wiener structure to reduce model complexity was introduced. This model structure was implemented in device level and tested in operation room for real-time anesthesia monitoring, diagnosis, and prediction. Furthermore, the impact of wireless channels on patient control in anesthesia applications was also investigated. Such a system involves communication channels which introduce noises due to quantization, channel noises, and have limited communication bandwidth resources. Usually signal averaging can be used effectively in reducing the noise effects. However, when feedback was intended, we showed that signal averaging will lose its utility substantially. To explain this phenomenon, we analyzed stability margins under signal averaging and derived some optimal strategies for selecting window sizes. Finally, a mathematical model for the auditory system was introduced to characterize the impact of anesthesia on auditory systems, and analyze and diagnose hearing damage. The auditory system was represented by a black-box input-output system with external sound stimuli as the input and the neuron firing rates as the output. Two parallel subsystem models were developed for modeling the auditory system dynamics: an ARX (Auto-Regression with External Input) model for the auditory system under external stimuli and an ARMA (Auto-Regression and Moving Average) model for the spontaneous activities of the neurons on primary auditory cortex. These models provide a quantitative characterization of anesthesia\u27s impacts and diagnosis of hearing loss on auditory transmission channels

Credibility Evidence for Computational Patient Models Used in the Development of Physiological Closed-Loop Controlled Devices for Critical Care Medicine

Physiological closed-loop controlled medical devices automatically adjust therapy delivered to a patient to adjust a measured physiological variable. In critical care scenarios, these types of devices could automate, for example, fluid resuscitation, drug delivery, mechanical ventilation, and/or anesthesia and sedation. Evidence from simulations using computational models of physiological systems can play a crucial role in the development of physiological closed-loop controlled devices; but the utility of this evidence will depend on the credibility of the computational model used. Computational models of physiological systems can be complex with numerous non-linearities, time-varying properties, and unknown parameters, which leads to challenges in model assessment. Given the wide range of potential uses of computational patient models in the design and evaluation of physiological closed-loop controlled systems, and the varying risks associated with the diverse uses, the specific model as well as the necessary evidence to make a model credible for a use case may vary. In this review, we examine the various uses of computational patient models in the design and evaluation of critical care physiological closed-loop controlled systems (e.g., hemodynamic stability, mechanical ventilation, anesthetic delivery) as well as the types of evidence (e.g., verification, validation, and uncertainty quantification activities) presented to support the model for that use. We then examine and discuss how a credibility assessment framework (American Society of Mechanical Engineers Verification and Validation Subcommittee, V&V 40 Verification and Validation in Computational Modeling of Medical Devices) for medical devices can be applied to computational patient models used to test physiological closed-loop controlled systems

AUTOMATED MEDICATION INFUSION SYSTEM DESIGN

Automated infusion of medications will be increasingly deployed in patient care as a means to deliver high-quality and continuous monitoring and therapy, and also to alleviate the excessive workload imposed on the clinicians. Therefore, a well-designed automated medication infusion system is an attractive alternative to today’s manual treatment requiring caregiver’s interventions. However, it also presents numerous challenges: 1) Significant inter- and intra-patient variability; 2) Complexity of medication infusion model; 3) Complexity of interaction of multiple medications; 4) Difficulty in coordination of medical targets. So the following approaches are proposed to address the various challenges: First, to deal with the large degree of individual patient variability, an adaptive controller was designed. This is because robust controllers which have fixed parameters might be difficult to offer decent behavior for all patients. Secondly, since classical adaptive controllers can only be applied to linearly parametrized models while even the infusion model of single drug is highly nonlinear and complex, a single-input single-output (SISO) semi-adaptive control approach which only adapt can adapt model parameters having a large impact on the model’s fidelity was introduced. Thirdly, the complicated interaction of multiple medications makes the adaptive controller for two medications even more difficult to design. So a model for two interacting dose responses was constructed and linearized at one operation point. Then the SISO semi-adaptive controller was extended to a two-input two-output case. However, this controller is only designed at one operating point. Therefore, based on two models associated with two distinct operating regimes, a two-model switching control technique was developed and combined with the semi-adaptive controller. Fourthly, we presented a coordinate mechanism to deal with the medical targets setting problem. In real clinical scenarios, the reference targets are empirically specified by caregivers, which are not always achievable in all patients. Therefore, our proposed coordinate mechanism can recursively adjusts the reference targets based on the estimated dose-response relationship of a patient. Lastly, we conducted some SISO control experiments on animals. Based on the experiments, we made some further improvements to the proposed controller