Artificial intelligence for the artificial kidney: Pointers to the future of a personalized hemodialysis therapy

Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment, or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve patient’s quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of Big Data and will require real-time predictive models. These may come from the fields of Machine Learning and Computational Intelligence, both included in Artificial Intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of Artificial Intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in Artificial Intelligence and Machine Learning, a scientific meeting was organized in the Hospital of Bellvitge (Barcelona, Spain). As an outcome of that meeting, the aim of this review is to investigate Artificial Intelligence experiences on dialysis, with a focus on potential barriers, challenges and prospects for future applications of these technologies.Postprint (author's final draft

Artificial intelligence for the artificial kidney: pointers to the future of a personalized hemodialysis therapy

Background: Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve the patient's quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters, and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for the assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of "big data" and will require real-time predictive models. These may come from the fields of machine learning and computational intelligence, both included in artificial intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of artificial intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in artificial intelligence and machine learning, a scientific meeting was organized in the Hospital Universitari Bellvitge (L'Hospitalet, Barcelona). As an outcome of that meeting, the aim of this review is to investigate artificial intel ligence experiences on dialysis, with a focus on potential barriers, challenges, and prospects for future applications of these technologies. Summary and Key Messages: Artificial intelligence research on dialysis is still in an early stage, and the main challenge relies on interpretability and/or comprehensibility of data models when applied to decision making. Artificial neural networks and medical decision support systems have been used to make predictions about anemia, total body water, or intradialysis hypotension and are promising approaches for the prescription and monitoring of hemodialysis therapy. Current dialysis machines are continuously improving due to innovative technological developments, but patient safety is still a key challenge. Real-time monitoring systems, coupled with automatic instantaneous biofeedback, will allow changing dialysis prescriptions continuously. The integration of vital sign monitoring with dialysis parameters will produce large data sets that will require the use of data analysis techniques, possibly from the area of machine learning, in order to make better decisions and increase the safety of patients

A radial basis function method for solving optimal control problems.

This work presents two direct methods based on the radial basis function (RBF) interpolation and arbitrary discretization for solving continuous-time optimal control problems: RBF Collocation Method and RBF-Galerkin Method. Both methods take advantage of choosing any global RBF as the interpolant function and any arbitrary points (meshless or on a mesh) as the discretization points. The first approach is called the RBF collocation method, in which states and controls are parameterized using a global RBF, and constraints are satisfied at arbitrary discrete nodes (collocation points) to convert the continuous-time optimal control problem to a nonlinear programming (NLP) problem. The resulted NLP is quite sparse and can be efficiently solved by well-developed sparse solvers. The second proposed method is a hybrid approach combining RBF interpolation with Galerkin error projection for solving optimal control problems. The proposed solution, called the RBF-Galerkin method, applies a Galerkin projection to the residuals of the optimal control problem that make them orthogonal to every member of the RBF basis functions. Also, RBF-Galerkin costate mapping theorem will be developed describing an exact equivalency between the Karush–Kuhn–Tucker (KKT) conditions of the NLP problem resulted from the RBF-Galerkin method and discretized form of the first-order necessary conditions of the optimal control problem, if a set of conditions holds. Several examples are provided to verify the feasibility and viability of the RBF method and the RBF-Galerkin approach as means of finding accurate solutions to general optimal control problems. Then, the RBF-Galerkin method is applied to a very important drug dosing application: anemia management in chronic kidney disease. A multiple receding horizon control (MRHC) approach based on the RBF-Galerkin method is developed for individualized dosing of an anemia drug for hemodialysis patients. Simulation results are compared with a population-oriented clinical protocol as well as an individual-based control method for anemia management to investigate the efficacy of the proposed method

Semi-blind robust indentification and robust control approach to personalized anemia management.

The homeostatic blood hemoglobin (Hb) content of a healthy individual varies between the range of 14-18 g/dL for a male and 12-16 g/dL for a female. This quantity provides an estimate of red blood cell (RBC) count in circulation at any given moment. RBC is a protein carrying substance that transports oxygen from the lungs to other tissues in the body and is synthesized by the kidney through a process known as erythropoiesis where erythropoietin is secreted in response to hypoxia. In this regard, the kidneys act not only as a controller but also as a sensor in regulating RBC levels. Patients with chronic kidney diseases (CKD) have dysfunctional kidneys that compromise these fundamental kidney functions. Consequently, anemia is developed. Anemics of CKD have low levels of Hb that must be controlled and properly regulated to the appropriate therapeutic range. Until the discovery of recombinant human erythropoietin (EPO) over three decades ago, treatment procedure of anemia conditions primarily involved repeated blood transfusions–a process known to be associated with several other health related complications. This discovery resulted in a paradigm shift in anemia management from blood transfusions to dosage therapies. The main objective of anemia management with EPO is to increase patients’ hemoglobin level from low to a suitable therapeutic range as defined by the National Kidney Foundation-Kidney Disease Outcomes Quality Initiative (NKF-KDOI) to be in the range of 10 - 12 g/dL while avoiding response values beyond 14 g/dL to prevent other complications associated with EPO medication. It is therefore imperative that clinicians balance dosage efficacy and toxicity in anemia management therapies. At most treatment facilities, protocols are developed to conform to NKF-KDOI recommendations. These protocols are generally based on EPO packet inserts and the expected Hb responses from the average patient. The inevitable variability within the patient group makes this “one-size-fits-all” dosing scheme non-optimal, at best, and potentially dangerous for certain group of patients that do not adhere to the notion of expected “average” response. A dosing strategy that is tailored to the individual patients’ response to EPO medication could provide a better alternative to the current treatment methods. An objective of this work is to develop EPO dosing strategies tailored to the individual patients using robust identification techniques and modern feedback control methods. First, a unique model is developed based on Hb responses and dosage EPO of the individual patients using semi-blind robust identification techniques. This provides a nominal model and a quantitative information on model uncertainty that accounts for other possible patient’s dynamics not considered in the modeling process. This is in the framework of generalized interpolation theory. Then, from the derived nominal model and the associated uncertainty information, robust controller is designed via the =H1-synthesis methods to provide a new dosing strategies for the individual patients. The H1 control theory has a feature of minimizing the influence of some unknown worst case gain disturbance on a system. Finally, a framework is provided to strategize dosing protocols for newly admitted patients

Current misconceptions in diagnosis and management of iron deficiency

The prevention and treatment of iron deficiency is a major public health goal. Challenges in the treatment of iron deficiency include finding and addressing the underlying cause and the selection of an iron replacement product which meets the needs of the patient. However, there are a number of non-evidence-based misconceptions regarding the diagnosis and management of iron deficiency, with or without anaemia, as well as inconsistency of terminology and lack of clear guidance on clinical pathways. In particular, the pathogenesis of iron deficiency is still frequently not addressed and iron not replaced, with indiscriminate red cell transfusion used as a default therapy. In our experience, this imprudent practice continues to be endorsed by non-evidence-based misconceptions. The intent of the authors is to provide a consensus that effectively challenges these misconceptions, and to highlight evidence-based alternatives for appropriate management (referred to as key points). We believe that this approach to the management of iron deficiency may be beneficial for both patients and healthcare systems. We stress that this paper solely presents the Authors' independent opinions. No pharmaceutical company funded or influenced the conception, development or writing of the manuscript

Approximate dynamic programming for anemia management.

The focus of this dissertation work is the formulation and improvement of anemia management process involving trial-and-error. A two-stage method is adopted toward this objective. Given a medical treatment process, a discrete Markov representation is first derived as a formal translation of the treatment process to a control problem under uncertainty. A simulative numerical solution of the control problem is then obtained on-the-fly in the form of a control law maximizing the long-term benefit at each decision stage. Approximate dynamic programming methods are employed in the proposed solution. The motivation underlying this choice is that, in reality, some patient characteristics, which are critical for the sake of treatment, cannot be determined through diagnosis and remain unknown until early stages of treatment, when the patient demonstrates them upon actions by the decision maker. A review of these simulative control tools, which are studied extensively in reinforcement learning theory, is presented. Two approximate dynamic programming tools, namely SARSA and Q -learning, are introduced. Their performance in discovering the optimal individualized drug dosing policy is illustrated on hypothetical patients made up as fuzzy models for simulations. As an addition to these generic reinforcement learning methods, a state abstraction scheme for the considered application domain is also proposed. The control methods of this study, capturing the essentials of a drug delivery problem, constitutes a novel computational framework for model-free medical treatment. Experimental evaluation of the dosing strategies produced by the proposed methods against the standard policy, which is being followed actually by human experts in Kidney Diseases Program, University of Louisville, shows the advantages for use of reinforcement learning in the drug dosing problem in particular and in medical decision making in general

UWOMJ Volume 27, No 3, June 1957

Schulich School of Medicine & Dentistryhttps://ir.lib.uwo.ca/uwomj/1045/thumbnail.jp