scholarly journals Deep learning for quantile regression under right censoring: DeepQuantreg

2021 ◽  
pp. 107323
Author(s):  
Yichen Jia ◽  
Jong-Hyeon Jeong
Keyword(s):  
Deep Learning ◽  
Quantile Regression ◽  
Right Censoring

scholarly journals A Deep Learning Framework for Automatic Meal Detection and Estimation in Artificial Pancreas Systems

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 466
Author(s):  
John Daniels ◽  
Pau Herrero ◽  
Pantelis Georgiou
Keyword(s):  
Deep Learning ◽  
Quantile Regression ◽  
Glucose Control ◽  
Closed Loop ◽  
Artificial Pancreas ◽  
Postprandial Glucose ◽  
User Engagement ◽  
Frequent User ◽  
Learning Framework ◽  
Closed Loop Systems

Current artificial pancreas (AP) systems are hybrid closed-loop systems that require manual meal announcements to manage postprandial glucose control effectively. This poses a cognitive burden and challenge to users with T1D since this relies on frequent user engagement to maintain tight glucose control. In order to move towards fully automated closed-loop glucose control, we propose an algorithm based on a deep learning framework that performs multitask quantile regression, for both meal detection and carbohydrate estimation. Our proposed method is evaluated in silico on 10 adult subjects from the UVa/Padova simulator with a Bio-inspired Artificial Pancreas (BiAP) control algorithm over a 2 month period. Three different configurations of the AP are evaluated -BiAP without meal announcement (BiAP-NMA), BiAP with meal announcement (BiAP-MA), and BiAP with meal detection (BiAP-MD). We present results showing an improvement of BiAP-MD over BiAP-NMA, demonstrating 144.5 ± 6.8 mg/dL mean blood glucose level (−4.4 mg/dL, p< 0.01) and 77.8 ± 6.3% mean time between 70 and 180 mg/dL (+3.9%, p< 0.001). This improvement in control is realised without a significant increase in mean in hypoglycaemia (+0.1%, p= 0.4). In terms of detection of meals and snacks, the proposed method on average achieves 93% precision and 76% recall with a detection delay time of 38 ± 15 min (92% precision, 92% recall, and 37 min detection time for meals only). Furthermore, BiAP-MD handles hypoglycaemia better than BiAP-MA based on CVGA assessment with fewer control errors (10% vs. 20%). This study suggests that multitask quantile regression can improve the capability of AP systems for postprandial glucose control without increasing hypoglycaemia.

scholarly journals Computer Model Calibration with Time Series Data Using Deep Learning and Quantile Regression

2022 ◽  
Vol 10 (1) ◽  
pp. 1-26
Author(s):  
Saumya Bhatnagar ◽  
Won Chang ◽  
Seonjin Kim ◽  
Jiali Wang
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Quantile Regression ◽  
Computer Model ◽  
Model Calibration ◽  
Time Series Data ◽  
Series Data

Quantile Regression

2005 ◽  
Author(s):  
Roger Koenker
Keyword(s):  
Quantile Regression

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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