1,720,973 research outputs found
Model-Based Techniques for Safety-Critical Events Detection in Type 1 Diabetes Therapy
No full textArtificial pancreas systems, also known as automated insulin delivery systems, are emerging therapeutic options for the management of type 1 diabetes, that automatically regulate blood glucose levels. Promptly detecting malfunctions and anomalies in artificial pancreas is essential for ensuring the safety, effectiveness, and reliability of the device, ultimately improving the quality of life of individuals with type 1 diabetes and reducing their overall healthcare burden associated with diabetes management. As a matter of fact, the efficacy of glucose regulation achieved through such systems can be significantly compromised in the event of hardware failures or incorrect interactions between users and artificial pancreas itself, potentially endangering the patient’s safety. Hence, the timely and reliable detection of system anomalies and malfunctions is of critical practical importance.
In this framework, this doctoral thesis focuses on the detection of anomalous events associated with the management of type 1 diabetes. These events encompass pressure-induced artifacts in glucose sensors, discontinuation of insulin delivery due to pump malfunctions, and user failures to communicate upcoming meals or physical activity to the system.
All the proposed detection methodologies rely on dynamic models of the system or the type 1 diabetic user, and are designed for both real-time and retrospective detection applications.
The effectiveness of these newly proposed detection strategies, along with a robustness analysis, were assessed using in-silico or real-world datasets. The first are generated through the UVa/Padova Type 1 Diabetes simulator, which has been accepted by the US Food and Drug Administration as a viable alternative to animal testing preceding human clinical trials with an artificial pancreas. The available real-world datasets have been collected through collaborations with Dexcom Inc. and Harvard University.
The proposed detection strategies are specifically designed to be integrated into a multi-module architecture aimed at identifying hardware-based malfunctions and dealing with the challenges posed by the human-in-the-loop aspects
Automatic Regulation of Anesthesia via Ultra-Local Model Control
Full text linkAs a part of the BMS2021 Benchmark Challenge, this paper deals with the design and testing of a closed-loop anesthesia delivery regulation system by exploiting the open-source Matlab-based patient simulator. Because of system inherent complexity together with intra-and inter-patient parameters variability and partially unknown disturbances, traditional model-based approaches may suffer. To overcome these limitations, we opt for a data-driven approach using real-time ultra-local models coupled with the corresponding so-called intelligent controllers. In this way, one maintains the hemodynamic variables while regulating the levels of hypnosis, analgesia, and neuromuscular blockade in anesthesia by automatic delivery of drugs. The performance of the proposed approach has been evaluated in silico by considering a representative dataset composed of 24 patients, the presence of disturbances mimicking both surgical stimulations and actions of “anesthesiologist in the loop”, including also noise effects and time-varying system delays
Detection of Glucose Sensor Faults in an Artificial Pancreas via Whiteness Test on Kalman Filter Residuals
Full text linkContinuous Glucose Monitoring (CGM) sensors are key components in an artificial pancreas, an emerging tool for type 1 diabetes treatment. Malfunctioning of this component might reduce the efficacy of glucose control achieved by the system and even pose the safety of the patient at risk. Therefore, accurate and prompt detection of these anomalies is an important problem. This paper investigates a model-based method to detect CGM failures. Based on an individualized linear model of the subject, identified on hystorical data, the method predicts future glucose concentration through a one-step ahead Kalman predictor. The correct functioning of the system is then monitored using two different criteria: the first checks the magnitude of prediction residuals. The second checks the whiteness of the residuals through a correlogram test. The effectiveness of the two criteria is investigated and compared by performing tests on an in-silico dataset obtained by means of UVA/Padova Type 1 Diabetes simulator, accepted by the US Food and Drug Administration as a substitute of animal testing prior to artificial pancreas clinical trials on humans
Mimicking the Complex Human Circulatory System via a Custom Hydro-mechanical Pulse Duplicator
No full textThe complex human systemic circulation can be mimicked by exploiting in vitro simulators, giving the chance to replicate both physiological and pathological conditions. Specifically, in this work, it is considered the Pulse Duplicator (PD) in use at the Healing Research Laboratory, at the University of Padova, Italy, aimed at testing medical devices, accelerating innovation cycles, and rapidly exploring new effective practical solutions. Besides the opportunities of such a workbench, there are also some challenges, especially for issues that are related to the set-up of experiments, in order to guarantee their quality and repeatability. It goes without saying that the proper tuning of certain PD parameters is crucial. This paper shows how one can assist the system tuning procedure by leveraging some tools of dynamical systems
Data-Driven Supervised Compression Artifacts Detection on Continuous Glucose Sensors
No full textContinuous Glucose Monitoring (CGM) sensors micro-invasively provide frequent glucose readings, improving the management of Type 1 diabetic patients' life and making available reach data-sets for retrospective analysis. Unlikely, CGM sensors are subject to failures, such as compression artifacts, that might impact on both real-time and respective CGM use. In this work is focused on retrospective detection of compression artifacts. An in-silico dataset is generated using the T1D UVa/Padova simulator and compression artifacts are subsequently added in known position, thus creating a dataset with perfectly accurate faulty/not-faulty labels. The problem of compression artifact detection is then faced with supervised data-driven techniques, in particular using Random Forest algorithm. The detection performance guaranteed by the method on in-silico data is satisfactory, opening the way for further analysis on real-data
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Unsupervised Retrospective Detection of Pressure Induced Failures in Continuous Glucose Monitoring Sensors for T1D Management
Full text linkContinuous Glucose Monitoring sensors (CGMs) have revolutionized type 1 diabetes (T1D) management. In particular, in several cases, the retrospective analysis of CGM recordings allows clinicians to review and adjust patients' therapy. However, in this set-up, the artifacts that are often present in CGM data could lead to incorrect therapeutic actions. To mitigate this risk, we investigate how to detect one of the most common of these artifacts, the so-called pressure induced sensor attenuations, by means of anomaly detection algorithms. Specifically, these methods belong to the class of unsupervised techniques, which is particularly appealing since it does not require a labeled dataset, hardly available in practice. After having designed five features to highlight the anomalous state of the sensor, 8 different methods (e.g. Isolation Forest and Histogram-based Outlier Score) are assessed both in silico using the UVa/Padova Type 1 Diabetes Simulator and on real data of 36 subjects monitored for about 10 days. In the in silico scenario, the best results are achieved with Isolation Forest, which recognized the 74% of the failures generating on average only 2 false alerts during the whole monitoring time. In real data, Isolation Forest is confirmed to be effective in the detection of failures, achieving a recall of 55% and generating 3 false alarms in 10 days. By allowing to detect more than 50% of the artifacts while discarding only a few portions of correct data in several days of monitoring, the proposed approach could effectively improve the quality of CGM data used by clinicians to retrospectively evaluate and adjust T1D therapy
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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