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Author: Scott Michael Pappada Publisher: ISBN: Category : Blood sugar monitoring Languages : en Pages : 267
Book Description
Critical care (e.g. trauma and cardiothoracic surgical) and diabetic patients are prone to variability in glucose concentration on a daily basis. Hypoglycemic and hyperglycemic glucose values in these patient populations have been associated with decreased patient outcomes. In diabetic patients, persistently elevated glucose values are associated with development of long term complications such as, but not limited to retinopathy, neuropathy, and nephropathy. In the critical care patient population, elevated glucose has been correlated to increases in mortality, length of stay in the intensive care unit (ICU), and morbidities. The maintenance of tight glycemic control in these patients without severe hypoglycemia or glycemic variability appears to improve outcomes in these patients. Various factors are associated with future glycemic excursions such as, but not limited to: lifestyle/activities (e.g. sleep-wake cycles), emotional factors (e.g. stress), nutritional intake, medication dosages, and ICU medical records (in critical care patients). In the field of diabetes research, models for prediction of glucose and/or models used to maintain tight glycemic control have been the focus of research. In the critical care patient population, very little research into development of such models has been completed to date. Multiple factors affect or are indicators of future glucose concentration. A suitable modeling technique needs to incorporate the effect of such factors for accurate prediction of glucose. A modeling technique well suited for this task is a neural network model. A neural network is an adapative modeling technique, which learns and updates model parameters based on determining patterns/trends existent in input data. This adapative capability, makes neural network modeling well suited for prediction of glucose where multiple factors impact future glycemic excursions. This dissertation summarizes the development and optimization of various neural network model architectures for the real-time prediction of glucose in diabetic and critical care patients. Neural network models were configured to predict glucose using prediction horizons>60 minutes, which have not been attained in many predictive models to date. The performance of the neural network model is assessed via determination of overall model error, percentage of glycemic extremes predicted, and clinical acceptability of model predictions as determined via Clarke Error Grid Analysis.
Author: Scott Michael Pappada Publisher: ISBN: Category : Blood sugar monitoring Languages : en Pages : 267
Book Description
Critical care (e.g. trauma and cardiothoracic surgical) and diabetic patients are prone to variability in glucose concentration on a daily basis. Hypoglycemic and hyperglycemic glucose values in these patient populations have been associated with decreased patient outcomes. In diabetic patients, persistently elevated glucose values are associated with development of long term complications such as, but not limited to retinopathy, neuropathy, and nephropathy. In the critical care patient population, elevated glucose has been correlated to increases in mortality, length of stay in the intensive care unit (ICU), and morbidities. The maintenance of tight glycemic control in these patients without severe hypoglycemia or glycemic variability appears to improve outcomes in these patients. Various factors are associated with future glycemic excursions such as, but not limited to: lifestyle/activities (e.g. sleep-wake cycles), emotional factors (e.g. stress), nutritional intake, medication dosages, and ICU medical records (in critical care patients). In the field of diabetes research, models for prediction of glucose and/or models used to maintain tight glycemic control have been the focus of research. In the critical care patient population, very little research into development of such models has been completed to date. Multiple factors affect or are indicators of future glucose concentration. A suitable modeling technique needs to incorporate the effect of such factors for accurate prediction of glucose. A modeling technique well suited for this task is a neural network model. A neural network is an adapative modeling technique, which learns and updates model parameters based on determining patterns/trends existent in input data. This adapative capability, makes neural network modeling well suited for prediction of glucose where multiple factors impact future glycemic excursions. This dissertation summarizes the development and optimization of various neural network model architectures for the real-time prediction of glucose in diabetic and critical care patients. Neural network models were configured to predict glucose using prediction horizons>60 minutes, which have not been attained in many predictive models to date. The performance of the neural network model is assessed via determination of overall model error, percentage of glycemic extremes predicted, and clinical acceptability of model predictions as determined via Clarke Error Grid Analysis.
Author: Eleni I. Georga Publisher: Academic Press ISBN: 0128051469 Category : Computers Languages : en Pages : 253
Book Description
Personalized Predictive Modeling in Diabetes features state-of-the-art methodologies and algorithmic approaches which have been applied to predictive modeling of glucose concentration, ranging from simple autoregressive models of the CGM time series to multivariate nonlinear regression techniques of machine learning. Developments in the field have been analyzed with respect to: (i) feature set (univariate or multivariate), (ii) regression technique (linear or non-linear), (iii) learning mechanism (batch or sequential), (iv) development and testing procedure and (v) scaling properties. In addition, simulation models of meal-derived glucose absorption and insulin dynamics and kinetics are covered, as an integral part of glucose predictive models. This book will help engineers and clinicians to: select a regression technique which can capture both linear and non-linear dynamics in glucose metabolism in diabetes, and which exhibits good generalization performance under stationary and non-stationary conditions; ensure the scalability of the optimization algorithm (learning mechanism) with respect to the size of the dataset, provided that multiple days of patient monitoring are needed to obtain a reliable predictive model; select a features set which efficiently represents both spatial and temporal dependencies between the input variables and the glucose concentration; select simulation models of subcutaneous insulin absorption and meal absorption; identify an appropriate validation procedure, and identify realistic performance measures. Describes fundamentals of modeling techniques as applied to glucose control Covers model selection process and model validation Offers computer code on a companion website to show implementation of models and algorithms Features the latest developments in the field of diabetes predictive modeling
Author: Boris Draznin Publisher: American Diabetes Association ISBN: 1580406572 Category : Medical Languages : en Pages : 338
Book Description
As the number of patients with diabetes increases annually, it is not surprising that the number of patients with diabetes who are admitted to the hospital also increases. Once in the hospital, patients with diabetes or hyperglycemia may be admitted to the Intensive Care Unit, require urgent or elective surgery, enteral or parenteral nutrition, intravenous insulin infusion, or therapies that significantly impact glycemic control (e.g., steroids). Because many clinical outcomes are influenced by the degree of glycemic control, knowledge of the best practices in inpatient diabetes management is extremely important. The field of inpatient management of diabetes and hyperglycemia has grown substantially in the last several years. This body of knowledge is summarized in this book, so it can reach the audience of hospitalists, endocrinologists, nurses and other team members who take care of hospitalized patients with diabetes and hyperglycemia.
Author: Philip Cryer Publisher: American Diabetes Association ISBN: 1580406491 Category : Medical Languages : en Pages : 194
Book Description
Intended for diabetes researchers and medical professionals who work closely with patients with diabetes, this newly updated and expanded edition provides new perspectives and direct insight into the causes and consequences of this serious medical condition from one of the foremost experts in the field. Using the latest scientific and medical developments and trends, readers will learn how to identify, prevent, and treat this challenging phenomenon within the parameters of the diabetes care regimen.
Author: Gary Scheiner Publisher: American Diabetes Association ISBN: 1580406262 Category : Medical Languages : en Pages : 130
Book Description
Use of real-time continuous glucose monitors among people with type 1 and type 2 diabetes is growing rapidly and should continue to grow until an artificial pancreas is brought to market. Likewise, use of professional systems in healthcare practices is expanding. But, other than manufacturer instructional manuals and some book chapters on CGMs, there are no standalone publications available with concise, non-commercial instructions on CGM prescription and use. Additionally, continuous glucose monitors are too often not used to their full and proper potential. This leaves users with suboptimal glucose control and can result in system abandonment. To address this, diabetes educator and author Gary Scheiner has created Practical CGM: Improving Patient Outcomes through Continuous Glucose Monitoring to give healthcare providers the skill to make more effective use of the data generated by continuous glucose monitors, in both real-time and on a retrospective analytic basis. Using a plain-language approach and distilling content to concise, practical tips and techniques, Scheiner has created a guide that will help practitioners optimize patient use of CGM systems and, ultimately, improve glucose control and patient health outcomes.
Author: Chris D. Geddes Publisher: Springer Science & Business Media ISBN: 0387330151 Category : Science Languages : en Pages : 460
Book Description
An essential reference for any laboratory working in the analytical fluorescence glucose sensing field. The increasing importance of these techniques is typified in one emerging area by developing non-invasive and continuous approaches for physiological glucose monitoring. This volume incorporates analytical fluorescence-based glucose sensing reviews, specialized enough to be attractive to professional researchers, yet appealing to a wider audience of scientists in related disciplines of fluorescence.
Author: L.B. Pape-Haugaard Publisher: IOS Press ISBN: 1643680838 Category : Medical Languages : en Pages : 1498
Book Description
Digital health and medical informatics have grown in importance in recent years, and have now become central to the provision of effective healthcare around the world. This book presents the proceedings of the 30th Medical Informatics Europe conference (MIE). This edition of the conference, hosted by the European Federation for Medical Informatics (EFMI) since the 1970s, was due to be held in Geneva, Switzerland in April 2020, but as a result of measures to prevent the spread of the Covid19 pandemic, the conference itself had to be cancelled. Nevertheless, because this collection of papers offers a wealth of knowledge and experience across the full spectrum of digital health and medicine, it was decided to publish the submissions accepted in the review process and confirmed by the Scientific Program Committee for publication, and these are published here as planned. The 232 papers are themed under 6 section headings: biomedical data, tools and methods; supporting care delivery; health and prevention; precision medicine and public health; human factors and citizen centered digital health; and ethics, legal and societal aspects. A 7th section deals with the Swiss personalized health network, and section 8 includes the 125 posters accepted for the conference. Offering an overview of current trends and developments in digital health and medical informatics, the book provides a valuable information resource for researchers and health practitioners alike.
Author: Clifford P. Shearman Publisher: Springer ISBN: 1447145259 Category : Medical Languages : en Pages : 227
Book Description
Public and political concern about the increasing prevalence of diabetes has prompted major concern about treatment of patients with the condition. Foot complications are some of the commonest causes of hospitalisation of people with diabetes and if not treated well often lead to amputation. There is evidence that 85% of these amputations can be prevented by better understanding of the problem and by multi-disciplinary teams working more effectively together. This has been recognised and NICE have recently published guidelines on diabetic foot complications as have Diabetes UK and NHS Diabetes. These have been successful in raising awareness of the problem but the local multi-disciplinary teams need clear practical advice on how to manage the foot in diabetes and deliver high quality care. With the current interest in improving outcomes for patients with foot complications this is an ideal time to make a practical evidence-based handbook available. This book will provide clear practical guidelines on how to manage all aspects of the foot in diabetes as well as an in-depth analysis of the most recent evidence. The book will be based on care pathways with algorithms for each section so it would be of practical value in any clinic in primary or secondary care. It will appeal to a wide range of health care professionals treating people with diabetes: vascular surgeons and trainees, orthopaedic surgeons, diabetes specialist nurses, podiatrists and tissue viability nurses.
Author: Elke Hattingen Publisher: Springer ISBN: 3642450407 Category : Medical Languages : en Pages : 166
Book Description
This book describes the basics, the challenges and the limitations of state of the art brain tumor imaging and examines in detail its impact on diagnosis and treatment monitoring. It opens with an introduction to the clinically relevant physical principles of brain imaging. Since MR methodology plays a crucial role in brain imaging, the fundamental aspects of MR spectroscopy, MR perfusion and diffusion-weighted MR methods are described, focusing on the specific demands of brain tumor imaging. The potential and the limits of new imaging methodology are carefully addressed and compared to conventional MR imaging. In the main part of the book, the most important imaging criteria for the differential diagnosis of solid and necrotic brain tumors are delineated and illustrated in examples. A closing section is devoted to the use of MR methods for the monitoring of brain tumor therapy. The book is intended for radiologists, neurologists, neurosurgeons, oncologists and other scientists in the biomedical field with an interest in neuro-oncology.
Author: Scott Michael Pappada
Author: Scott Michael Pappada
Author: Eleni I. Georga
Author: Boris Draznin
Author: Philip Cryer
Author: Gary Scheiner
Author: Mark A. Sperling
Author: Chris D. Geddes
Author: L.B. Pape-Haugaard
Author: Clifford P. Shearman
Author: Elke Hattingen