Learning Insulin-Glucose Dynamics in the Wild

08/06/2020
by   Andrew C. Miller, et al.
0

We develop a new model of insulin-glucose dynamics for forecasting blood glucose in type 1 diabetics. We augment an existing biomedical model by introducing time-varying dynamics driven by a machine learning sequence model. Our model maintains a physiologically plausible inductive bias and clinically interpretable parameters – e.g., insulin sensitivity – while inheriting the flexibility of modern pattern recognition algorithms. Critical to modeling success are the flexible, but structured representations of subject variability with a sequence model. In contrast, less constrained models like the LSTM fail to provide reliable or physiologically plausible forecasts. We conduct an extensive empirical study. We show that allowing biomedical model dynamics to vary in time improves forecasting at long time horizons, up to six hours, and produces forecasts consistent with the physiological effects of insulin and carbohydrates.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/03/2021

SIMLR: Machine Learning inside the SIR model for COVID-19 Forecasting

Accurate forecasts of the number of newly infected people during an epid...
research
07/15/2022

Greykite: Deploying Flexible Forecasting at Scale at LinkedIn

Forecasts help businesses allocate resources and achieve objectives. At ...
research
05/17/2018

Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks

We conduct an extensive empirical study on short-term electricity price ...
research
12/18/2021

GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics

We consider the problem of probabilistic forecasting over categories wit...
research
11/12/2019

Modeling Constrained Preemption Dynamics Of Transient Cloud Servers

In this paper, we conduct a first of its kind empirical study and statis...
research
10/29/2019

Deep Integro-Difference Equation Models for Spatio-Temporal Forecasting

Integro-difference equation (IDE) models describe the conditional depend...
research
09/23/2020

Dense Forecasting of Wildfire Smoke Particulate Matter Using Sparsity Invariant Convolutional Neural Networks

Accurate forecasts of fine particulate matter (PM 2.5) from wildfire smo...

Please sign up or login with your details

Forgot password? Click here to reset