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Personalized glucose forecasting for type 2 diabetes using data assimilation

Overview of attention for article published in PLoS Computational Biology, April 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

news
10 news outlets
twitter
29 X users
patent
3 patents
facebook
3 Facebook pages
reddit
1 Redditor

Citations

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84 Dimensions

Readers on

mendeley
145 Mendeley
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Title
Personalized glucose forecasting for type 2 diabetes using data assimilation
Published in
PLoS Computational Biology, April 2017
DOI 10.1371/journal.pcbi.1005232
Pubmed ID
Authors

David J. Albers, Matthew Levine, Bruce Gluckman, Henry Ginsberg, George Hripcsak, Lena Mamykina

Abstract

Type 2 diabetes leads to premature death and reduced quality of life for 8% of Americans. Nutrition management is critical to maintaining glycemic control, yet it is difficult to achieve due to the high individual differences in glycemic response to nutrition. Anticipating glycemic impact of different meals can be challenging not only for individuals with diabetes, but also for expert diabetes educators. Personalized computational models that can accurately forecast an impact of a given meal on an individual's blood glucose levels can serve as the engine for a new generation of decision support tools for individuals with diabetes. However, to be useful in practice, these computational engines need to generate accurate forecasts based on limited datasets consistent with typical self-monitoring practices of individuals with type 2 diabetes. This paper uses three forecasting machines: (i) data assimilation, a technique borrowed from atmospheric physics and engineering that uses Bayesian modeling to infuse data with human knowledge represented in a mechanistic model, to generate real-time, personalized, adaptable glucose forecasts; (ii) model averaging of data assimilation output; and (iii) dynamical Gaussian process model regression. The proposed data assimilation machine, the primary focus of the paper, uses a modified dual unscented Kalman filter to estimate states and parameters, personalizing the mechanistic models. Model selection is used to make a personalized model selection for the individual and their measurement characteristics. The data assimilation forecasts are empirically evaluated against actual postprandial glucose measurements captured by individuals with type 2 diabetes, and against predictions generated by experienced diabetes educators after reviewing a set of historical nutritional records and glucose measurements for the same individual. The evaluation suggests that the data assimilation forecasts compare well with specific glucose measurements and match or exceed in accuracy expert forecasts. We conclude by examining ways to present predictions as forecast-derived range quantities and evaluate the comparative advantages of these ranges.

X Demographics

X Demographics

The data shown below were collected from the profiles of 29 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 145 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Austria 1 <1%
Unknown 144 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 22%
Researcher 20 14%
Student > Master 17 12%
Student > Bachelor 11 8%
Student > Doctoral Student 10 7%
Other 22 15%
Unknown 33 23%
Readers by discipline Count As %
Computer Science 25 17%
Engineering 22 15%
Medicine and Dentistry 13 9%
Biochemistry, Genetics and Molecular Biology 10 7%
Agricultural and Biological Sciences 9 6%
Other 30 21%
Unknown 36 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 87. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 October 2022.
All research outputs
#486,050
of 25,382,440 outputs
Outputs from PLoS Computational Biology
#350
of 8,960 outputs
Outputs of similar age
#10,109
of 323,433 outputs
Outputs of similar age from PLoS Computational Biology
#18
of 150 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has done particularly well, scoring higher than 96% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 323,433 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 150 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.