Skip to main content
SpringerLink
Log in

Novel Indicators for Adverse Glycemic Events Detection Analysis Based on Continuous Glucose Monitoring Neural Network Predictive Models

  • Article
  • Published:
Journal of Shanghai Jiaotong University (Science) Aims and scope Submit manuscript

Abstract

This paper proposes five indicators to evaluate the effectiveness and viability for adverse glycemic events detection based on predicted blood glucose (BG) values. False negative rate (FNR) and false positive rate (FPR) are defined to evaluate whether it can detect adverse glycemic events (AGEs) based on the predicted value. The temporal overlap (TO) and time difference (TD) are proposed to evaluate whether the predicted model can capture the accurate time duration of AGEs. The sum of squared percent (SSP) measures comprehensive similarity between prediction values and true values. We examined 328 patients with type 2 diabetes, containing real continuous glucose monitoring data with 5-minute time intervals. Autoregressive integrated moving average model has lower FNR and FPR. The gated recurrent unit has better temporal behavior where the mean TO with standard deviation is calculated as 0.84±0.18, and the mean TD with standard deviation is (4.39±4.01) min. Neural models have better effects on SSP (for hypoglycemia, long-short tern memory possesses 0.149 and 0.246). These five indicators are able to evaluate whether we can detect abnormal BG levels and reveal the temporal behavior of AGEs effectively. The proposed neural predictive models have more promising application in AGE detection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. HAAK T, HANAIRE H, AJJAN R, et al. Flash glucose-sensing technology as a replacement for blood glucose monitoring for the management of insulintreated type 2 diabetes: A multicenter, open-label randomized controlled trial [J]. Diabetes Therapy, 2017, 8(1): 55–73.

    Article  Google Scholar 

  2. CHARLEER S, DE BLOCK C, VAN HUFFEL L, et al. Quality of life and glucose control after 1 year of nationwide reimbursement of intermittently scanned continuous glucose monitoring in adults living with type 1 diabetes (FUTURE): A prospective observational real-world cohort study [J]. Diabetes Care, 2020, 43(2): 389–397.

    Article  Google Scholar 

  3. MONNIER L, COLETTE C, OWENS D R. The application of simple metrics in the assessment of glycaemic variability [J]. Diabetes & Metabolism, 2018, 44(4): 313–319.

    Article  Google Scholar 

  4. LISZKA-HACKZELL J J. Prediction of blood glucose levels in diabetic patients using a hybrid AI technique [J]. Computers and Biomedical Research, 1999, 32(2): 132–144.

    Article  Google Scholar 

  5. CHUAH Z M, PARAMESRAN R, THAMBIRATNAM K, et al. A two-level partial least squares system for non-invasive blood glucose concentration prediction [J]. Chemometrics and Intelligent Laboratory Systems, 2010, 104(2): 347–351.

    Article  Google Scholar 

  6. GYUK P, VASSÁNYI I, KÓSA I. Blood glucose level prediction for diabetics based on nutrition and insulin administration logs using personalized mathematical models [J]. Journal of Healthcare Engineering, 2019, 2019: 8605206.

    Article  Google Scholar 

  7. CONTRERAS I, OVIEDO S, VETTORETTI M, et al. Personalized blood glucose prediction: A hybrid approach using grammatical evolution and physiological models [J]. PLoS ONE, 2017, 12(11): e0187754.

    Article  Google Scholar 

  8. ZHAO H, ZHAO C H, YU C X, et al. Multiple order model migration and optimal model selection for online glucose prediction in Type 1 diabetes [J]. AIChE Journal, 2018, 64(3): 822–834.

    Article  Google Scholar 

  9. ZHAO C H, YU C X. Rapid model identification for online subcutaneous glucose concentration prediction for new subjects with type I diabetes [J]. IEEE Transactions on Bio-Medical Engineering, 2015, 62(5): 1333–1344.

    Article  Google Scholar 

  10. HAYES C, KRISKA A. Role of physical activity in diabetes management and prevention [J]. Journal of the American Dietetic Association, 2008, 108(4): S19–S23.

    Article  Google Scholar 

  11. RODBARD D. Continuous glucose monitoring: A review of recent studies demonstrating improved glycemic outcomes [J]. Diabetes Technology & Therapeutics, 2017, 19(S3): S25–S37.

    Article  Google Scholar 

  12. FACCHINETTI A. Continuous glucose monitoring sensors: Past, present and future algorithmic challenges [J]. Sensors, 2016, 16(12): 2093.

    Article  Google Scholar 

  13. ZECCHIN C, FACCHINETTI A, SPARACINO G, et al. Reduction of number and duration of hypoglycemic events by glucose prediction methods: A proof-of-concept in silico study [J]. Diabetes Technology & Therapeutics, 2013, 15(1): 66–77.

    Article  Google Scholar 

  14. ZHONG A, CHOUDHARY P, MCMAHON C, et al. Effectiveness of automated insulin management features of the MiniMed ® 640G sensor-augmented insulin pump [J]. Diabetes Technology & Therapeutics, 2016, 18(10): 657–663.

    Article  Google Scholar 

  15. BERGENSTAL R M, GARG S, WEINZIMER S A, et al. Safety of a hybrid closed-loop insulin delivery system in patients with type 1 diabetes [J]. JAMA, 2016, 316(13): 1407.

    Article  Google Scholar 

  16. HAMDI T, BEN ALI J, DI COSTANZO V, et al. Accurate prediction of continuous blood glucose based on support vector regression and differential evolution algorithm [J]. Biocybernetics and Biomedical Engineering, 2018, 38(2): 362–372.

    Article  Google Scholar 

  17. SISODIA D, SISODIA D S. Prediction of diabetes using classification algorithms [J]. Procedia Computer Science, 2018, 132: 1578–1585.

    Article  Google Scholar 

  18. JUNG M, LEE Y B, JIN S M, et al. Prediction of daytime hypoglycemic events using continuous glucose monitoring data and classification technique [EB/OL]. (2017-04-27). https://arxiv.org/abs/1704.08769.

  19. YEH H C, BROWN T T, MARUTHUR N, et al. Comparative effectiveness and safety of methods of insulin delivery and glucose monitoring for diabetes mellitus: A systematic review and meta-analysis [J]. Annals of Internal Medicine, 2012, 157(5): 336–347.

    Article  Google Scholar 

  20. POLONSKY W H, HESSLER D, RUEDY K J, et al. The impact of continuous glucose monitoring on markers of quality of life in adults with type 1 diabetes: Further findings from the DIAMOND randomized clinical trial [J]. Diabetes Care, 2017, 40(6): 736–741.

    Article  Google Scholar 

  21. LIU C Y, VEHí J, AVARI P, et al. Long-term glucose forecasting using a physiological model and deconvolution of the continuous glucose monitoring signal [J]. Sensors, 2019, 19(19): 4338.

    Article  Google Scholar 

  22. GADALETA M, FACCHINETTI A, GRISAN E, et al. Prediction of adverse glycemic events from continuous glucose monitoring signal [J]. IEEE Journal of Biomedical and Health Informatics, 2019, 23(2): 650–659.

    Article  Google Scholar 

  23. SAKURAI K, KAWAI Y, YAMAZAKI M, et al. Prediction of lowest nocturnal blood glucose level based on self-monitoring of blood glucose in Japanese patients with type 2 diabetes [J]. Journal of Diabetes and its Complications, 2018, 32(12): 1118–1123.

    Article  Google Scholar 

  24. GOTO A, ARAH O A, GOTO M, et al. Severe hypoglycaemia and cardiovascular disease: Systematic review and meta-analysis with bias analysis [J]. BMJ, 2013, 347: f4533.

    Article  Google Scholar 

  25. INVESTIGATORS N S S, FINFER S, LIU B, et al. Hypoglycemia and risk of death in critically ill patients [J]. The New England Journal of Medicine, 2012, 367(12): 1108–1118.

    Article  Google Scholar 

  26. LU J Y, MA X J, ZHOU J, et al. Association of time in range, as assessed by continuous glucose monitoring, with diabetic retinopathy in type 2 diabetes [J]. Diabetes Care, 2018, 41(11): 2370–2376.

    Article  Google Scholar 

  27. NGUYEN M, HAN J L, SPANAKIS E K, et al. A review of continuous glucose monitoring-based composite metrics for glycemic control [J]. Diabetes Technology & Therapeutics, 2020, 22(8): 613–622.

    Article  Google Scholar 

  28. YANG J, LI L, SHI Y M, et al. An ARIMA model with adaptive orders for predicting blood glucose concentrations and hypoglycemia [J]. IEEE Journal of Biomedical and Health Informatics, 2019, 23(3): 1251–1260.

    Article  Google Scholar 

  29. HOCHREITER S, SCHMIDHUBER J. Long short-term memory [J]. Neural Computation, 1997, 9(8): 1735–1780.

    Article  Google Scholar 

  30. SIAMI-NAMINI S, TAVAKOLI N, SIAMI NAMIN A. A comparison of ARIMA and LSTM in forecasting time series [C]//2018 17th IEEE International Conference on Machine Learning and Applications. Orlando, FL, USA: IEEE, 2018: 1394–1401.

    Google Scholar 

  31. CHO K, VAN MERRIENBOER B, GULCEHRE C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation [EB/OL]. (2014-06-03). https://arxiv.org/abs/1406.1078.

  32. SHIH S Y, SUN F K, LEE H Y. Temporal pattern attention for multivariate time series forecasting [J]. Machine Learning, 2019, 108(8/9): 1421–1441.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, Shanghai, 200237, China

    Guannan Lu  (卢冠男) & Mengling Wang  (王梦灵)

  2. INTERGRIS Mental Health Center, Spencer, OK, 73084, USA

    Tamara Fox

  3. Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, 200237, China

    Peng Jiang  (蒋 鹏) & Fusong Jiang  (蒋伏松)

Authors
  1. Guannan Lu  (卢冠男)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mengling Wang  (王梦灵)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tamara Fox
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peng Jiang  (蒋 鹏)
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fusong Jiang  (蒋伏松)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mengling Wang  (王梦灵).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, G., Wang, M., Fox, T. et al. Novel Indicators for Adverse Glycemic Events Detection Analysis Based on Continuous Glucose Monitoring Neural Network Predictive Models. J. Shanghai Jiaotong Univ. (Sci.) 27, 498–504 (2022). https://doi.org/10.1007/s12204-022-2439-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12204-022-2439-0

Key words

CLC number

Document code

Search

Navigation