Background: Estimation of future glucose concentrations is a crucial task for diabetes management. Predicted glucose values can be used for early hypoglycemic/hyperglycemic alarms or for adjustment of insulin injections or insulin infusion rates of manual or automated pumps. Continuous glucose monitoring (CGM) technologies provide glucose readings at a high frequency and consequently detailed insight into the subject's glucose variations. The objective of this research is to develop reliable subject-specific glucose prediction models using CGM data. Methods: Two separate patient databases collected under hospitalized (disturbance-free) and normal daily life conditions are used for validation of the proposed glucose prediction algorithm. Both databases consist of glucose concentration data collected at 5-min intervals using a CGM device. Using time-series analysis, low-order linear models are developed from patients' own CGM data. The time-series models are integrated with recursive identification...

Estimation of future glucose concentrations with subject-specific recursive linear models.

Discusses closed-loop and partially closed-loop control strategies for insulin delivery and measuring glucose concentration. The authors review the subcutaneous closed- and partially closed-loop strategies that have been proposed and tested in recent years for insulin-dependent diabetes therapy. Focus is on control, modeling, and information technology aspects, and future directions of research are also outlined. This survey complements earlier reviews concerning control approaches and application of computers in diabetes care.

The subcutaneous route to insulin dependent diabetes therapy

A diabetes care management system for managing blood glucose levels associated with diabetes comprising a computing device and an insulin delivery device. The computing device generally includes (i) a memory comprising one or more optimal blood glucose values, one or more self care values of a patient, one or more measured blood glucose values, and one or more scaling factors for weighting the impact on a future blood glucose value and that are customizable to an individual patient to predict the effect on the blood glucose of self care actions performed by the individual patient; (ii) a microprocessor, in communication with the memory, programmed to (A) determine the one or more scaling factors from one or more physiological parameters including body mass, metabolism rate, fitness level or hepatic or peripheral insulin sensitivity, or combinations thereof, and (B) calculate a further value, the further value being based on the self care values, and on the one or more optimal blood glucose values, and on the one or more scaling factors; and (iii) a display configured to display information according to the further value; and (iv) a housing, wherein the memory and the microprocessor are housed within the housing, thereby providing a hand-held, readily transportable computing device. The insulin delivery device may deliver insulin in response to information associated with the further value.

Diabetes care management system

Signal detection theory (SDT) assumes a division of objective truths or "states of the world" into the nonoverlapping categories of signal and noise. The definition of a signal in many real settings, however, varies with context and over time. In the terminology of fuzzy logic, a real-world signal has a value that falls in a range between unequivocal presence and unequivocal absence. The definition of a response can also be nonbinary. Accordingly the methods of fuzzy logic can be combined with SDT, yielding fuzzy SDT. We describe the basic postulates of fuzzy SDT and provide formulas for fuzzy analysis of detection performance, based on four steps: (a) selection of mapping functions for signal and response; (b) use of mixed-implication functions to assign degrees of membership in hits, false alarms, misses, and correct rejections; (c) computation of fuzzy hit, false alarm, miss, and correct rejection rates; and (d) computation of fuzzy sensitivity and bias measures. Fuzzy SDT can considerably extend the range and utility of SDT by handling the contextual and temporal variability of most real-world signals. Actual or potential applications of fuzzy SDT include evaluation of the performance of human, machine, and human-machine detectors in real systems.

Fuzzy signal detection theory: basic postulates and formulas for analyzing human and machine performance.

Insulin-Dependent Diabetes Mellitus (IDDM) is a chronic disease characterized by the inability of the pancreas to produce sufficient amounts of insulin. Daily compensation of the deficiency requires 4-6 insulin injections to be taken daily, the aim of this insulin therapy being to maintain normoglycemia - i.e., a blood glucose level between 4 and 7mmol/l. To determine the quantity and timing of these injections, various different approaches are used. Currently, mostly qualitative and semi-quantitative models and reasoning are used to design such a therapy. Here, an attempt is made to show how system identification and control may be used to estimate predictive quantitative models to be used in design of optimal insulin regimens. The system was divided into three subsystems, the insulin subsystem, the glucose subsystem and the insulin-glucose interaction. The insulin subsystem aims to describe the absorption of injected insulin from the subcutaneous depots and the glucose subsystem the absorption of glucose from the gut following a meal. These subsystems were modeled using compartment models and proposed models found in the literature. Several black-box models and grey-box models describing the insulin/glucose interaction were developed and analyzed. These models were fitted to real data monitored by an IDDM patient. Many difficulties were encountered, typical of biomedical systems: Non-uniform and scarce sampling, time-varying dynamics and severe nonlinearities were some of the difficulties encountered during the modeling. None of the proposed models were able to describe the system accurately in all aspects during all conditions. However, all the linear models shared some dynamics. Based on the estimated models, short-term blood glucose predictors for up to two-hour-ahead blood glucose prediction were designed. Furthermore, we explored the issues that arise when applying prediction theory and control to short-term blood glucose prediction.

Diabetes mellitus modeling and short-term prediction based on blood glucose measurements.

This paper describes the improvements in r-HuEPO therapy of uremic patients that may be obtained by using a mathematical model of patient response together with a delivery control strategy derived from the theory of industrial control. A mathematical model of r-HuEPO action is presented, and its applicability to dialytic patients is shown. Moreover, a new statistical technique for identifying the parameters of the mathematical model analyzing a patient population is summarized, and a control strategy for r-HuEPO delivery in uremic patients based on a Fuzzy Set Controller is introduced. Some results obtained from simulation, are presented.

Mathematical modeling of erythropoietin therapy in uremic anemia. Does it improve cost-effectiveness?

Analysis & Validation of Mean Value Models for SI IC-Engines

Deals with the design of intelligent controllers for drug delivery in long term treatment, where a partially closed-loop is necessary in order to assist patients and physicians in assessing and modifying drug protocols. The authors present the general characteristics of their proposed approach and an application of predictive fuzzy controllers for managing renal anaemia.

Predictive fuzzy controllers for drug delivery

Adaptive Drug Dosage in Long Term Treatment by using Fuzzy Controllers and Bayesian Networks

Adaptive Pole-Placement Control with Input Saturations

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

C. Siviero

Author Tools

Chat about Author