Abstract

Many factors affect blood glucose levels in type 1 diabetics, several of which vary largely both in magnitude and delay of the effect. Modern rapid-acting insulins generally have a peak time after 60–90 min, while carbohydrate intake can affect blood glucose levels more rapidly for high glycemic index foods, or slower for other carbohydrate sources. It is important to have good estimates of the development of glucose levels in the near future both for diabetic patients managing their insulin distribution manually, as well as for closed-loop systems making decisions about the distribution. Modern continuous glucose monitoring systems provide excellent sources of data to train machine learning models to predict future glucose levels. In this paper, we present an approach for predicting blood glucose levels for diabetics up to 1 h into the future. The approach is based on recurrent neural networks trained in an end-to-end fashion, requiring nothing but the glucose level history for the patient. Our approach obtains results that are comparable to the state of the art on the Ohio T1DM dataset for blood glucose level prediction. In addition to predicting the future glucose value, our model provides an estimate of its certainty, helping users to interpret the predicted levels. This is realized by training the recurrent neural network to parameterize a univariate Gaussian distribution over the output. The approach needs no feature engineering or data preprocessing and is computationally inexpensive. We evaluate our method using the standard root-mean-squared error (RMSE) metric, along with a blood glucose-specific metric called the surveillance error grid (SEG). We further study the properties of the distribution that is learned by the model, using experiments that determine the nature of the certainty estimate that the model is able to capture.

Highlights

  • Our future will be recorded and quantified in unprecedented temporal resolution

  • We trained and evaluated our method on the Ohio T1DM dataset for blood glucose level prediction [16]

  • The glucose level of patient 575 is harder to predict than the glucose level for patient 570, as seen in Table 2 where the mean root-mean-squared error (RMSE) for patient 570 is 15.959 and the mean RMSE for patient 575 is 21.675

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Summary

Introduction

Our future will be recorded and quantified in unprecedented temporal resolution. A rapidly increasing variety of variables gets stored, describing activities we engage in as well as physiological and medical phenomena. Blood glucose levels adhere to complex dynamics that depend on many different variables (such as carbohydrate intake, recent insulin injections, physical activity, stress levels, the presence of an infection in the body, sleeping patterns, hormonal patterns, etc) [4, 9]. This makes predicting the short-term blood glucose changes (up to a few hours) a challenging task, and developing machine learning (ML) approaches an obvious approach for improving patient care. They present a possibly more scalable solution to the myriad of machine learning problems in precision health management resulting from technology changes alone

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