20 research outputs found

    Online Glucose Prediction in Type-1 Diabetes by Neural Network Models

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    Diabetes mellitus is a chronic disease characterized by dysfunctions of the normal regulation of glucose concentration in the blood. In Type 1 diabetes the pancreas is unable to produce insulin, while in Type 2 diabetes derangements in insulin secretion and action occur. As a consequence, glucose concentration often exceeds the normal range (70-180 mg/dL), with short- and long-term complications. Hypoglycemia (glycemia below 70 mg/dL) can progress from measurable cognition impairment to aberrant behaviour, seizure and coma. Hyperglycemia (glycemia above 180 mg/dL) predisposes to invalidating pathologies, such as neuropathy, nephropathy, retinopathy and diabetic foot ulcers. Conventional diabetes therapy aims at maintaining glycemia in the normal range by tuning diet, insulin infusion and physical activity on the basis of 4-5 daily self-monitoring of blood glucose (SMBG) measurements, obtained by the patient using portable minimally-invasive lancing sensor devices. New scenarios in diabetes treatment have been opened in the last 15 years, when minimally invasive continuous glucose monitoring (CGM) sensors, able to monitor glucose concentration in the subcutis continuously (i.e. with a reading every 1 to 5 min) over several days (7-10 consecutive days), entered clinical research. CGM allows tracking glucose dynamics much more effectively than SMBG and glycemic time-series can be used both retrospectively, e.g. to optimize metabolic control therapy, and in real-time applications, e.g. to generate alerts when glucose concentration exceeds the normal range thresholds or in the so-called “artificial pancreas”, as inputs of the closed loop control algorithm. For what concerns real time applications, the possibility of preventing critical events is, clearly, even more appealing than just detecting them as they occur. This would be doable if glucose concentration were known in advance, approximately 30-45 min ahead in time. The quasi continuous nature of the CGM signal renders feasible the use of prediction algorithms which could allow the patient to take therapeutic decisions on the basis of future instead of current glycemia, possibly mitigating/ avoiding imminent critical events. Since the introduction of CGM devices, various methods for short-time prediction of glucose concentration have been proposed in the literature. They are mainly based on black box time series models and the majority of them uses only the history of the CGM signal as input. However, glucose dynamics are influenced by many factors, e.g. quantity of ingested carbohydrates, administration of drugs including insulin, physical activity, stress, emotions and inter- and intra-individual variability is high. For these reasons, prediction of glucose time course is a challenging topic and results obtained so far may be improved. The aim of this thesis is to investigate the possibility of predicting future glucose concentration, in the short term, using new models based on neural networks (NN) exploiting, apart from CGM history, other available information. In particular, we first develop an original model which uses, as inputs, the CGM signal and information on timing and carbohydrate content of ingested meals. The prediction algorithm is based on a feedforward NN in parallel with a linear predictor. Results are promising: the predictor outperforms widely used state of art techniques and forecasts are accurate and allow obtaining a satisfactory time anticipation. Then we propose a second model, which exploits a different NN architecture, a jump NN, which combines benefits of both feedforward NN and linear algorithm obtaining performance similar to the previously developed predictor, although the simpler structure. To conclude the analysis, information on doses of injected bolus of insulin are added as input of the jump NN and the relative importance of every input signal in determining the NN output is investigated by developing an original sensitivity analysis. All the proposed predictors are assessed on real data of Type 1 diabetics, collected during the European FP7 project DIAdvisor. To evaluate the clinical usefulness of prediction in improving diabetes management we also propose a new strategy to quantify, using an in silico environment, the reduction of hypoglycemia when alerts and relative therapy are triggered on the basis of prediction, obtained with our NN algorithm, instead of CGM. Finally, possible inclusion of additional pieces of information such as physical activity is investigated, though at a preliminary level. The thesis is organized as follows. Chapter 1 gives an introduction to the diabetes disease and the current technologies for CGM, presents state of art techniques for short-time prediction of glucose concentration of diabetics and states the aim and the novelty of the thesis. Chapter 2 discusses NN paradigms from a theoretical point of view and specifies technical details common to the design and implementation of all the NN algorithms proposed in the following. Chapter 3 describes the first prediction model we propose, based on a NN in parallel with a linear algorithm. Chapter 4 presents an alternative simpler architecture, based on a jump NN, and demonstrates its equivalence, in terms of performance, with the previously proposed algorithm. Chapter 5 further improves the jump NN, by adding new inputs and investigating their effective utility by a sensitivity analysis. Chapter 6 points out possible future developments, as the possibility of exploiting information on physical activity, reporting also a preliminary analysis. Finally, Chapter 7 describes the application of NN for generation of preventive hypoglycemic alerts and evaluates improvement of diabetes management in a simulated environment. Some concluding remarks end the thesis

    Medical Informatics and Data Analysis

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    During recent years, the use of advanced data analysis methods has increased in clinical and epidemiological research. This book emphasizes the practical aspects of new data analysis methods, and provides insight into new challenges in biostatistics, epidemiology, health sciences, dentistry, and clinical medicine. This book provides a readable text, giving advice on the reporting of new data analytical methods and data presentation. The book consists of 13 articles. Each article is self-contained and may be read independently according to the needs of the reader. The book is essential reading for postgraduate students as well as researchers from medicine and other sciences where statistical data analysis plays a central role

    Aerospace medicine and biology: A cumulative index to a continuing bibliography (supplement 345)

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    This publication is a cumulative index to the abstracts contained in Supplements 333 through 344 of Aerospace Medicine and Biology: A Continuing Bibliography. Seven indexes are included -- subject, personal author, corporate source, foreign technology, contract number, report number, and accession number

    A modeling platform to predict cancer survival and therapy outcomes using tumor tissue derived metabolomics data.

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    Cancer is a complex and broad disease that is challenging to treat, partially due to the vast molecular heterogeneity among patients even within the same subtype. Currently, no reliable method exists to determine which potential first-line therapy would be most effective for a specific patient, as randomized clinical trials have concluded that no single regimen may be significantly more effective than others. One ongoing challenge in the field of oncology is the search for personalization of cancer treatment based on patient data. With an interdisciplinary approach, we show that tumor-tissue derived metabolomics data is capable of predicting clinical response to systemic therapy classified as disease control vs. progressive disease and pathological stage classified as stage I/II/III vs. stage IV via data analysis with machine-learning techniques (AUROC = 0.970; AUROC=0.902). Patient survival was also analyzed via statistical methods and machine-learning, both of which show that tumor-tissue derived metabolomics data is capable of risk stratifying patients in terms of long vs. short survival (OS AUROC = 0.940TEST; PFS AUROC = 0.875TEST). A set of key metabolites as potential biomarkers and associated metabolic pathways were also found for each outcome, which may lead to insight into biological mechanisms. Additionally, we developed a methodology to calibrate tumor growth related parameters in a well-established mathematical model of cancer to help predict the potential nuances of chemotherapeutic response. The proposed methodology shows results consistent with clinical observations in predicting individual patient response to systemic therapy and helps lay the foundation for further investigation into the calibration of mathematical models of cancer with patient-tissue derived molecular data. Chapters 6 and 8 were published in the Annals of Biomedical Engineering. Chapters 2, 3, and 7 were published in Metabolomics, Lung Cancer, and Pharmaceutical Research, respectively. Chapters 4 has been accepted for publication at the journal Metabolomics (in press) and Chapter 5 is in review at the journal Metabolomics. Chapter 9 is currently undergoing preparation for submission

    Ramon Llull's Ars Magna

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    Proceedings of the 10th International Chemical and Biological Engineering Conference - CHEMPOR 2008

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    This volume contains full papers presented at the 10th International Chemical and Biological Engineering Conference - CHEMPOR 2008, held in Braga, Portugal, between September 4th and 6th, 2008.FC

    NASA Thesaurus. Volume 1: Hierarchical listing

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    There are 16,713 postable terms and 3,716 nonpostable terms approved for use in the NASA scientific and technical information system in the Hierarchical Listing of the NASA Thesaurus. The generic structure is presented for many terms. The broader term and narrower term relationships are shown in an indented fashion that illustrates the generic structure better than the more widely used BT and NT listings. Related terms are generously applied, thus enhancing the usefulness of the Hierarchical Listing. Greater access to the Hierarchical Listing may be achieved with the collateral use of Volume 2 - Access Vocabulary

    NASA thesaurus. Volume 1: Hierarchical Listing

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    There are over 17,000 postable terms and nearly 4,000 nonpostable terms approved for use in the NASA scientific and technical information system in the Hierarchical Listing of the NASA Thesaurus. The generic structure is presented for many terms. The broader term and narrower term relationships are shown in an indented fashion that illustrates the generic structure better than the more widely used BT and NT listings. Related terms are generously applied, thus enhancing the usefulness of the Hierarchical Listing. Greater access to the Hierarchical Listing may be achieved with the collateral use of Volume 2 - Access Vocabulary and Volume 3 - Definitions

    EUROSENSORS XVII : book of abstracts

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    Fundação Calouste Gulbenkien (FCG).Fundação para a Ciência e a Tecnologia (FCT)
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