ACCIAROLI, GIADA
 Distribuzione geografica
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Continente #
NA - Nord America 1.428
EU - Europa 344
AS - Asia 231
OC - Oceania 2
Totale 2.005
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Nazione #
US - Stati Uniti d'America 1.427
CN - Cina 118
IT - Italia 86
SG - Singapore 78
DE - Germania 63
SE - Svezia 52
FR - Francia 50
IE - Irlanda 28
RU - Federazione Russa 25
GB - Regno Unito 11
JP - Giappone 10
FI - Finlandia 8
HK - Hong Kong 5
IN - India 5
MT - Malta 5
ID - Indonesia 4
IR - Iran 4
NO - Norvegia 4
ES - Italia 3
UA - Ucraina 3
AU - Australia 2
KR - Corea 2
TR - Turchia 2
AT - Austria 1
BE - Belgio 1
CA - Canada 1
CH - Svizzera 1
CZ - Repubblica Ceca 1
EE - Estonia 1
IQ - Iraq 1
MY - Malesia 1
NL - Olanda 1
TW - Taiwan 1
Totale 2.005
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Città #
Fairfield 223
Chandler 174
Ashburn 140
Ann Arbor 106
Woodbridge 90
Houston 88
Seattle 74
Wilmington 66
Singapore 60
Cambridge 59
Boardman 33
Beijing 29
Dublin 28
Medford 28
Princeton 28
Padova 27
Roxbury 27
Des Moines 26
Santa Clara 19
Nanjing 17
San Diego 16
New York 9
Helsinki 7
Cagliari 6
Montesarchio 6
Sagamihara 6
Nanchang 5
Parsippany 5
Shenyang 5
Tianjin 5
Isfahan 4
Jakarta 4
Kristiansand 4
Dearborn 3
Durham 3
Hong Kong 3
Jinan 3
Kharkiv 3
Mikazura 3
Modena 3
Oxford 3
Paris 3
Pietà 3
Pittsburgh 3
Storrs 3
Ahmedabad 2
Bassano del Grappa 2
Central 2
Eskisehir 2
Hangzhou 2
Hefei 2
Imsida 2
Indio 2
Kilburn 2
Milan 2
Nam-gu 2
Naples 2
Ningbo 2
Norwalk 2
Piacenza 2
Pioltello 2
San Jose 2
Taizhou 2
Valencia 2
Abano Terme 1
Baotou 1
Bari 1
Bergamo 1
Burgessville 1
Cedar Lake 1
Changzhou 1
Chengdu 1
Dronten 1
Easton 1
Falls Church 1
Forest City 1
Guangzhou 1
Kirkuk 1
Kunming 1
Legnaro 1
Madrid 1
Mahbubnagar 1
Manchester 1
Martellago 1
Monte San Giovanni Campano 1
Moscow 1
Nanning 1
Ogden 1
Osnabrück 1
Providence 1
Redditch 1
Redwood City 1
Rockville 1
Shanghai 1
Tallinn 1
Venice 1
Vidor 1
Wuhan 1
Zurich 1
Totale 1.533
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Nome #
Calibration of continuous glucose monitoring sensors by time-varying models and Bayesian estimation 169
A Model of Acetaminophen Pharmacokinetics and its Effect on Continuous Glucose Monitoring Sensor Measurements 136
From two to one per day calibration of Dexcom G4 platinum by a time-varying day-specific Bayesian prior 120
Head-to-head comparison of the accuracy of Abbott FreeStyle Libre and Dexcom G5 mobile 117
Glycaemic variability-based classification of impaired glucose tolerance vs. type 2 diabetes using continuous glucose monitoring data 111
Toward Calibration-Free Continuous Glucose Monitoring Sensors: Bayesian Calibration Approach Applied to Next-Generation Dexcom Technology 104
Wearable continuous glucose monitoring sensors: A revolution in diabetes treatment 100
Diabetes and Prediabetes Classification Using Glycemic Variability Indices From Continuous Glucose Monitoring Data 95
Retrospective Continuous-Time Blood Glucose Estimation in Free Living Conditions with a Non-Invasive Multisensor Device 90
Reduction of Blood Glucose Measurements to Calibrate Subcutaneous Glucose Sensors: A Bayesian Multiday Framework 86
Simple Linear Support Vector Machine Classifier Can Distinguish Impaired Glucose Tolerance Versus Type 2 Diabetes Using a Reduced Set of CGM-Based Glycemic Variability Indices 85
Continuous Glucose Monitoring: Current Use in Diabetes Management and Possible Future Applications 82
Calibration of minimally invasive continuous glucose monitoring sensors: State-of-the-art and current perspectives 68
Support vector machine fed by CGM-based glycemic variability indices can distinguish between IGT and T2D subjects 63
IGT and T2D subjects automatically classified using a selection of CGM-based glycemic variability indices 58
Good accuracy of CGM-based glucose variability indices for IGT and T2D classification 55
Next generation Dexcom sensor with Bayesian algorithm goes towards a calibration-free scenario 53
Bayesian Model Selection Framework to Improve Calibration of Continuous Glucose Monitoring Sensors for Diabetes Management 52
CGM-based glycemic variability indices allow accurate classification of IGT and T2D subjects 51
Head to head accuracy comparison between two interstitial glucose sensors 49
Bayesian calibration algorithm for next generation Dexcom sensor: 8.4% MARD with one calibration every 4 days 46
Glucose sensors for diabetes management: no need of 24h warm-up time by the use of Bayes estimation 41
New Bayesian algorithm to reduce calibrations in continuous glucose monitoring sensors 41
CALIBRATIONS OF DEXCOM G4 PLATINUM REDUCED TO ONE PER DAY BY A TIME-VARYING DAY-SPECIFIC BAYESIAN PRIOR 40
Smart calibration of continuous glucose monitoring sensors: utility of Bayes estimation 38
Non-Invasive Continuous-Time Blood Pressure Estimation from a Single Channel PPG Signal using Regularized ARX Models 35
Calibration of Dexcom G4 Platinum reduced to one per day by a time-varying day-specific Bayesian prior 34
Reduced calibrations and maintained accuracy on next generation CGM compared to Dexcom G5: results using a Bayesian approach 34
Totale 2.053
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Categoria #
all - tutte 7.047
article - articoli 3.579
book - libri 0
conference - conferenze 0
curatela - curatele 0
other - altro 0
patent - brevetti 0
selected - selezionate 0
volume - volumi 0
Totale 10.626
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Totale Lug Ago Sett Ott Nov Dic Gen Feb Mar Apr Mag Giu
2019/2020244 0 0 0 0 44 22 41 35 42 32 23 5
2020/2021327 36 30 3 15 32 10 9 10 59 44 64 15
2021/2022373 7 47 19 23 29 39 4 57 22 20 16 90
2022/2023367 49 54 11 29 81 51 2 27 40 3 13 7
2023/2024202 8 22 17 20 2 17 7 50 3 4 21 31
2024/2025198 3 81 33 45 36 0 0 0 0 0 0 0
Totale 2.053