Literature DB >> 33584164

The OhioT1DM Dataset for Blood Glucose Level Prediction: Update 2020.

Cindy Marling1, Razvan Bunescu1.   

Abstract

This paper documents the OhioT1DM Dataset, which was developed to promote and facilitate research in blood glucose level prediction. It contains eight weeks' worth of continuous glucose monitoring, insulin, physiological sensor, and self-reported life-event data for each of 12 people with type 1 diabetes. An associated graphical software tool allows researchers to visualize the integrated data. The paper details the contents and format of the dataset and tells interested researchers how to obtain it. The OhioT1DM Dataset was first released in 2018 for the first Blood Glucose Level Prediction (BGLP) Challenge. At that time, the dataset was half its current size, containing data for only six people with type 1 diabetes. Data for an additional six people is being released in 2020 for the second BGLP Challenge. This paper subsumes and supersedes the paper which documented the original dataset.

Entities:  

Year:  2020        PMID: 33584164      PMCID: PMC7881904     

Source DB:  PubMed          Journal:  CEUR Workshop Proc        ISSN: 1613-0073


  3 in total

1.  Use of case-based reasoning to enhance intensive management of patients on insulin pump therapy.

Authors:  Frank L Schwartz; Jay H Shubrook; Cynthia R Marling
Journal:  J Diabetes Sci Technol       Date:  2008-07

2.  Using LSTMs to learn physiological models of blood glucose behavior.

Authors:  Sadegh Mirshekarian; Razvan Bunescu; Cindy Marling; Frank Schwartz
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2017-07

3.  LSTMs and Neural Attention Models for Blood Glucose Prediction: Comparative Experiments on Real and Synthetic Data.

Authors:  Sadegh Mirshekarian; Hui Shen; Razvan Bunescu; Cindy Marling
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2019-07
  3 in total
  10 in total

1.  Predicting Glycaemia in Type 1 Diabetes Patients: Experiments in Feature Engineering and Data Imputation.

Authors:  Jouhyun Jeon; Peter J Leimbigler; Gaurav Baruah; Michael H Li; Yan Fossat; Alfred J Whitehead
Journal:  J Healthc Inform Res       Date:  2019-12-10

2.  Predicting Progression Patterns of Type 2 Diabetes using Multi-sensor Measurements.

Authors:  Ramin Ramazi; Christine Perndorfer; Emily C Soriano; Jean-Philippe Laurenceau; Rahmatollah Beheshti
Journal:  Smart Health (Amst)       Date:  2021-06-12

3.  Comparing Machine Learning Techniques for Blood Glucose Forecasting Using Free-living and Patient Generated Data.

Authors:  Hadia Hameed; Samantha Kleinberg
Journal:  Proc Mach Learn Res       Date:  2020-08

4.  Selection of Noninvasive Features in Wrist-Based Wearable Sensors to Predict Blood Glucose Concentrations Using Machine Learning Algorithms.

Authors:  Brian Bogue-Jimenez; Xiaolei Huang; Douglas Powell; Ana Doblas
Journal:  Sensors (Basel)       Date:  2022-05-06       Impact factor: 3.847

Review 5.  The Potential of Current Noninvasive Wearable Technology for the Monitoring of Physiological Signals in the Management of Type 1 Diabetes: Literature Survey.

Authors:  Elena Daskalaki; Anne Parkinson; Nicola Brew-Sam; Md Zakir Hossain; David O'Neal; Christopher J Nolan; Hanna Suominen
Journal:  J Med Internet Res       Date:  2022-04-08       Impact factor: 7.076

6.  Stacked LSTM based deep recurrent neural network with kalman smoothing for blood glucose prediction.

Authors:  Md Fazle Rabby; Yazhou Tu; Md Imran Hossen; Insup Lee; Anthony S Maida; Xiali Hei
Journal:  BMC Med Inform Decis Mak       Date:  2021-03-16       Impact factor: 2.796

7.  Evaluation of interpretability methods for multivariate time series forecasting.

Authors:  Ozan Ozyegen; Igor Ilic; Mucahit Cevik
Journal:  Appl Intell (Dordr)       Date:  2021-07-27       Impact factor: 5.019

8.  Blood Glucose Prediction with Variance Estimation Using Recurrent Neural Networks.

Authors:  John Martinsson; Alexander Schliep; Björn Eliasson; Olof Mogren
Journal:  J Healthc Inform Res       Date:  2019-12-01

9.  Generation of Individualized Synthetic Data for Augmentation of the Type 1 Diabetes Data Sets Using Deep Learning Models.

Authors:  Josep Noguer; Ivan Contreras; Omer Mujahid; Aleix Beneyto; Josep Vehi
Journal:  Sensors (Basel)       Date:  2022-06-30       Impact factor: 3.847

Review 10.  Commercial and Scientific Solutions for Blood Glucose Monitoring-A Review.

Authors:  Yirui Xue; Angelika S Thalmayer; Samuel Zeising; Georg Fischer; Maximilian Lübke
Journal:  Sensors (Basel)       Date:  2022-01-06       Impact factor: 3.576

  10 in total

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