Explainable machine learning for real-time Hypoglycemia and Hyperglycemia prediction and personalized control recommendations
Explainable machine learning for real-time Hypoglycemia and Hyperglycemia prediction and personalized control recommendations
Background: The occurrences of acute complications arising from hypoglycemia and hyperglycemia peak as young adults with type 1 diabetes (T1D) take control of their own care. Continuous glucose monitoring (CGM) devices provide real-time glucose readings enabling users to manage their control proactively. Machine learning algorithms can use CGM data to make ahead-of-time risk predictions and provide insight into an individual’s longer term control. Methods: We introduce explainable machine learning to make predictions of hypoglycemia (<70 mg/dL) and hyperglycemia (>270 mg/dL) up to 60 minutes ahead of time. We train our models using CGM data from 153 people living with T1D in the CITY (CGM Intervention in Teens and Young Adults With Type 1 Diabetes)survey totaling more than 28 000 days of usage, which we summarize into (short-term, medium-term, and long-term) glucose control features along with demographic information. We use machine learning explanations (SHAP [SHapley Additive exPlanations]) to identify which features have been most important in predicting risk per user. Results: Machine learning models (XGBoost) show excellent performance at predicting hypoglycemia (area under the receiver operating curve [AUROC]: 0.998, average precision: 0.953) and hyperglycemia (AUROC: 0.989, average precision: 0.931) in comparison with a baseline heuristic and logistic regression model. Conclusions: Maximizing model performance for glucose risk prediction and management is crucial to reduce the burden of alarm fatigue on CGM users. Machine learning enables more precise and timely predictions in comparison with baseline models. SHAP helps identify what about a CGM user’s glucose control has led to predictions of risk which can be used to reduce their long-term risk of complications.
continuous glucose monitoring, explainable and trustworthy AI, feature extraction, hyperglycemia prediction, hypoglycemia prediction, machine learning
Duckworth, Christopher
992c216c-8f66-48a8-8de6-2f04b4f736e6
Guy, Matthew J.
e58c4b88-d281-4bf0-acd0-df603d650349
Kumaran, Anitha
c7880b22-4f22-4d76-9272-0b21f7778192
O’Kane, Aisling Ann
b9607ee4-f626-49df-b621-9f13fe244e60
Ayobi, Amid
83cefe47-631e-4a0b-bbf3-1e75f149972c
Chapman, Adriane
721b7321-8904-4be2-9b01-876c430743f1
Marshall, Paul
864b012f-09b6-49cf-a442-dde2ea28a2d7
Boniface, Michael
f30bfd7d-20ed-451b-b405-34e3e22fdfba
Duckworth, Christopher
992c216c-8f66-48a8-8de6-2f04b4f736e6
Guy, Matthew J.
e58c4b88-d281-4bf0-acd0-df603d650349
Kumaran, Anitha
c7880b22-4f22-4d76-9272-0b21f7778192
O’Kane, Aisling Ann
b9607ee4-f626-49df-b621-9f13fe244e60
Ayobi, Amid
83cefe47-631e-4a0b-bbf3-1e75f149972c
Chapman, Adriane
721b7321-8904-4be2-9b01-876c430743f1
Marshall, Paul
864b012f-09b6-49cf-a442-dde2ea28a2d7
Boniface, Michael
f30bfd7d-20ed-451b-b405-34e3e22fdfba
Duckworth, Christopher, Guy, Matthew J., Kumaran, Anitha, O’Kane, Aisling Ann, Ayobi, Amid, Chapman, Adriane, Marshall, Paul and Boniface, Michael
(2022)
Explainable machine learning for real-time Hypoglycemia and Hyperglycemia prediction and personalized control recommendations.
Journal of Diabetes Science and Technology.
(doi:10.1177/19322968221103561).
Abstract
Background: The occurrences of acute complications arising from hypoglycemia and hyperglycemia peak as young adults with type 1 diabetes (T1D) take control of their own care. Continuous glucose monitoring (CGM) devices provide real-time glucose readings enabling users to manage their control proactively. Machine learning algorithms can use CGM data to make ahead-of-time risk predictions and provide insight into an individual’s longer term control. Methods: We introduce explainable machine learning to make predictions of hypoglycemia (<70 mg/dL) and hyperglycemia (>270 mg/dL) up to 60 minutes ahead of time. We train our models using CGM data from 153 people living with T1D in the CITY (CGM Intervention in Teens and Young Adults With Type 1 Diabetes)survey totaling more than 28 000 days of usage, which we summarize into (short-term, medium-term, and long-term) glucose control features along with demographic information. We use machine learning explanations (SHAP [SHapley Additive exPlanations]) to identify which features have been most important in predicting risk per user. Results: Machine learning models (XGBoost) show excellent performance at predicting hypoglycemia (area under the receiver operating curve [AUROC]: 0.998, average precision: 0.953) and hyperglycemia (AUROC: 0.989, average precision: 0.931) in comparison with a baseline heuristic and logistic regression model. Conclusions: Maximizing model performance for glucose risk prediction and management is crucial to reduce the burden of alarm fatigue on CGM users. Machine learning enables more precise and timely predictions in comparison with baseline models. SHAP helps identify what about a CGM user’s glucose control has led to predictions of risk which can be used to reduce their long-term risk of complications.
Text
19322968221103561
- Version of Record
More information
Accepted/In Press date: 2022
e-pub ahead of print date: 13 June 2022
Additional Information:
Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: We acknowledge funding from UKRI Trustworthy Autonomous Systems Hub (grant code: RITM0372366).
Publisher Copyright:
© 2022 Diabetes Technology Society.
Keywords:
continuous glucose monitoring, explainable and trustworthy AI, feature extraction, hyperglycemia prediction, hypoglycemia prediction, machine learning
Identifiers
Local EPrints ID: 472191
URI: http://eprints.soton.ac.uk/id/eprint/472191
ISSN: 1932-2968
PURE UUID: 3153c324-5573-49e3-ae41-31c68fd02608
Catalogue record
Date deposited: 29 Nov 2022 17:32
Last modified: 17 Mar 2024 04:06
Export record
Altmetrics
Contributors
Author:
Christopher Duckworth
Author:
Matthew J. Guy
Author:
Anitha Kumaran
Author:
Aisling Ann O’Kane
Author:
Amid Ayobi
Author:
Paul Marshall
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics