1,720,968 research outputs found
Prediction of blood glucose concentrations and hypoglycemic events in Type 1 Diabetes by linear and nonlinear algorithms
Full text linkIl diabete di tipo 1 (T1D) è una malattia metabolica caratterizzata da una mancanza di produzione di insulina che provoca un’alterazione dei livelli di glucosio nel sangue (BG). Di conseguenza, per mantenere la glicemia in un adeguato range fisiologico (generalmente [70-180] mg/dL) durante la giornata, i soggetti diabetici devono somministrarsi insulina esogena, assumere carboidrati ad azione rapida, seguire una dieta equilibrata ed eseguire attività fisica. Infatti, limitare le escursioni della glicemia consente di ridurre il rischio di mortalità e le conseguenze, a lungo e breve termine, causate da eventi iperglicemici (BG > 180 mg/dL) e ipoglicemici (BG 180 mg/dL) and hypoglycemia (i.e., BG<70 mg/dL). Minimally invasive continuous glucose monitoring (CGM) sensors have become a widely used tool by T1D individuals to keep track, and eventually correct, their BG levels. These devices provide frequent BG measurements (commonly one every 5 minutes) for several days, and embed visual and acoustic alerts when the hypo-/hyperglycemic thresholds are crossed, thus helping patients in taking corrective actions like hypotreatments and corrective insulin boluses. However, timely preventive alerts coupled with targeted corrective strategies would be even more helpful to avoid or mitigate the onset of impending, adverse events. For this reason, the real-time forecasting of BG levels has a key role in the development of i) advanced decision support systems (DSS), which are software for helping patients in the decision-making process, and ii) artificial pancreas systems (APS), which are devices for automatizing insulin delivery. The large plethora of data provided by CGM devices (but also insulin pumps, wearable devices, electronic diaries and dedicated mobile applications), coupled with the technological advancements in artificial intelligence, have driven the diabetes technology community to intensively focus on developing glucose predictive algorithms, exploiting methodologies already employed in the fields of time series forecasting, system identification, machine and deep learning. Among the possible approaches for glucose prediction, two main categories can be identified: algorithms fed only by the past history of the CGM signal or fed by CGM data plus additional information such as insulin, carbohydrates or physical exercise. One main open issue is that none of the literature studies have systematically investigated how and/or how much different input information as well as complex algorithms contribute to improve glucose prediction on datasets recorded in daily-life conditions. To address this gap, this PhD thesis presents the development and application of several linear and nonlinear algorithms for the forecasting of BG levels and hypoglycemic events, and investigates how and how much different input information and model complexity play a role in the prediction
A Deep-Learning Based Algorithm for the Management of Hyperglycemia in Type 1 Diabetes Therapy
No full textdrCORRECT: An Algorithm for the Preventive Administration of Postprandial Corrective Insulin Boluses in Type 1 Diabetes Management
No full textBackground: In type 1 diabetes therapy, precise tuning of postprandial corrective insulin boluses (CIBs) is crucial to mitigate hyperglycemia without inducing dangerous hypoglycemic events. Several heuristic formulas accounting for continuous glucose monitoring (CGM) trend have been proposed in the literature. However, these formulas suggest a lot of quantized CIB adjustments, and they lack personalization. Method: drCORRECT algorithm proposed in this work employs a patient-specific time parameter and the "dynamic risk" (DR) measure to determine postprandial CIB suggestion. The expected benefits include the reduction of time in hyperglycemia, thanks to the preventive action exploited through DR. drCORRECT has been assessed retrospectively vs the literature methods proposed by Aleppo et al (AL), Bruttomesso et al (BR), and Ziegler et al (ZI) using a data set of 49 CGM daily traces recorded in free-living conditions. Retrospective evaluation of the algorithms is made possible by the use of ReplayBG, a digital twin-based tool that allows assessing alternative insulin therapies on already collected glucose data. Efficacy in terms of glucose control was measured by temporal, risk indicators, and dedicated hyperglycemic/hypoglycemic events metrics. Results: drCORRECT significantly reduces time spent in hyperglycemia when compared with AL and BR (33.52 [24.16, 39.89]% vs 39.76 [22.54, 48.15]% and 36.32 [26.91, 45.93]%, respectively); significantly reduces daily injected insulin (5.97 [3.80, 8.06] U vs 7.5 [5.21, 10.34] U), glycemia risk index (38.78 [26.58, 55.39] vs 40.78 [27.95, 70.30]), and time spent in hypoglycemia (0.00 [0.00, 1.74]% vs 0.00 [0.00, 10.23]%) when compared with ZI, resulting overall in a safer strategy. Conclusions: The proposed drCORRECT algorithm allows preventive actions thanks to the personalized timing configuration and the introduction of the innovative DR-based CIB threshold, proving to be a valid alternative to the available heuristic literature methods
Forecasting postbariatric hypoglycaemia in Roux-en-Y gastric bypass patients using model-based algorithms fed by continuous glucose monitoring data: a proof-of-concept study.
Full text linkA Correction Insulin Bolus Delivery Strategy for Decision Support Systems in Type 1 Diabetes
No full text: Management of type 1 diabetes (T1D) requires affected individuals to perform multiple daily actions to keep their blood glucose levels within the safe rage and avoid adverse hypo-/hyperglycemic episodes. Decision support systems (DSS) for T1D are composite tools that implement multiple software modules aiming to ease such a burden and to improve glucose control. At the University of Padova, we are developing a new DSS that currently integrate a smart insulin bolus calculator for optimal insulin dosing and a rescue carbohydrate intake advisor to tackle hypoglycemia. However, a module specifically targeting hyperglycemia, that suggests the administration of corrective insulin boluses (CIB), is still missing. For such a scope, this work aims to assess a recent literature methodology, proposed by Aleppo et al., which provides a simple strategy for dealing with hyperglycemia. The methodology is tested retrospectively on clinical data of individuals with T1D. In particular, here we leveraged a novel in silico tool that first identifies a non-linear model of glucose-insulin dynamics on data, then uses such model to simulate and compare the glucose trace obtained by "replaying" the recorded scenario and the glucose trace obtained using the CIB delivery strategy under evaluation. Results show that the CIB delivery strategy significantly reduce the percentage of time spent in hyperglycemia (-15.63%) without inducing any hypoglycemic episode, demonstrating both safety and efficacy of its use. These preliminary results suggest that the CIB delivery strategy proposed by Aleppo et al. is a promising candidate to be included in our system to counteract hyperglycemia. Future work will extensively evaluate the methodology and will compare it against other competing approaches
Relationship Between Symptom Perception and Postprandial Glycemic Profiles in Patients With Postbariatric Hypoglycemia After Roux-en-Y Gastric Bypass Surgery.
No full textOBJECTIVE
Post-bariatric surgery hypoglycemia (PBH) is a metabolic complication of Roux-en-Y gastric bypass (RYGB). Since symptoms are a key component of the Whipple's triad to diagnose nondiabetic hypoglycemia, we evaluated the relationship between self-reported symptoms and postprandial sensor glucose profiles.
RESEARCH DESIGN AND METHODS
Thirty patients with PBH after RYGB (age: 50.1 [41.6-60.6] years, 86.7% female, BMI: 26.5 [23.5-31.2] kg/m2; median [interquartile range]) wore a blinded Dexcom G6 sensor while recording autonomic, neuroglycopenic, and gastrointestinal symptoms over 50 days. Symptoms (overall and each type) were categorized into those occurring in postprandial periods (PPPs) without hypoglycemia, or in the preceding dynamic or hypoglycemic phase of PPPs with hypoglycemia (nadir sensor glucose <3.9 mmol/L). We further explored the relationship between symptoms and the maximum negative rate of sensor glucose change and nadir sensor glucose levels.
RESULTS
In 5,851 PPPs, 775 symptoms were reported, of which 30.6 (0.0-59.9)% were perceived in PPPs without hypoglycemia, 16.7 (0.0-30.1)% in the preceding dynamic phase and 45.0 (13.7-84.7)% in the hypoglycemic phase of PPPs with hypoglycemia. Per symptom type, 53.6 (23.8-100.0)% of the autonomic, 30.0 (5.6-80.0)% of the neuroglycopenic, and 10.4 (0.0-50.0)% of the gastrointestinal symptoms occurred in the hypoglycemic phase of PPPs with hypoglycemia. Both faster glucose dynamics and lower nadir sensor glucose levels were related with symptom perception.
CONCLUSIONS
The relationship between symptom perception and PBH is complex, challenging clinical judgement and decision-making in this population
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
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
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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