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Mosquera-Lopez C, Jacobs PG. Digital twins and artificial intelligence in metabolic disease research. Trends Endocrinol Metab 2024:S1043-2760(24)00113-9. [PMID: 38744606 DOI: 10.1016/j.tem.2024.04.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/25/2024] [Accepted: 04/25/2024] [Indexed: 05/16/2024]
Abstract
Digital twin technology is emerging as a transformative paradigm for personalized medicine in the management of chronic conditions. In this article, we explore the concept and key characteristics of a digital twin and its applications in chronic non-communicable metabolic disease management, with a focus on diabetes case studies. We cover various types of digital twin models, including mechanistic models based on ODEs, data-driven ML algorithms, and hybrid modeling strategies that combine the strengths of both approaches. We present successful case studies demonstrating the potential of digital twins in improving glucose outcomes for individuals with T1D and T2D, and discuss the benefits and challenges of translating digital twin research applications to clinical practice.
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Affiliation(s)
- Clara Mosquera-Lopez
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
| | - Peter G Jacobs
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA.
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2
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Kozinetz RM, Berikov VB, Semenova JF, Klimontov VV. Machine Learning and Deep Learning Models for Nocturnal High- and Low-Glucose Prediction in Adults with Type 1 Diabetes. Diagnostics (Basel) 2024; 14:740. [PMID: 38611653 PMCID: PMC11011674 DOI: 10.3390/diagnostics14070740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 03/06/2024] [Accepted: 03/28/2024] [Indexed: 04/14/2024] Open
Abstract
Glucose management at night is a major challenge for people with type 1 diabetes (T1D), especially for those managed with multiple daily injections (MDIs). In this study, we developed machine learning (ML) and deep learning (DL) models to predict nocturnal glucose within the target range (3.9-10 mmol/L), above the target range, and below the target range in subjects with T1D managed with MDIs. The models were trained and tested on continuous glucose monitoring data obtained from 380 subjects with T1D. Two DL algorithms-multi-layer perceptron (MLP) and a convolutional neural network (CNN)-as well as two classic ML algorithms, random forest (RF) and gradient boosting trees (GBTs), were applied. The resulting models based on the DL and ML algorithms demonstrated high and similar accuracy in predicting target glucose (F1 metric: 96-98%) and above-target glucose (F1: 93-97%) within a 30 min prediction horizon. Model performance was poorer when predicting low glucose (F1: 80-86%). MLP provided the highest accuracy in low-glucose prediction. The results indicate that both DL (MLP, CNN) and ML (RF, GBTs) algorithms operating CGM data can be used for the simultaneous prediction of nocturnal glucose values within the target, above-target, and below-target ranges in people with T1D managed with MDIs.
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Affiliation(s)
| | | | | | - Vadim V. Klimontov
- Laboratory of Endocrinology, Research Institute of Clinical and Experimental Lymphology—Branch of the Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (RICEL–Branch of IC&G SB RAS), 630060 Novosibirsk, Russia; (R.M.K.); (V.B.B.); (J.F.S.)
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Jacobs PG, Herrero P, Facchinetti A, Vehi J, Kovatchev B, Breton MD, Cinar A, Nikita KS, Doyle FJ, Bondia J, Battelino T, Castle JR, Zarkogianni K, Narayan R, Mosquera-Lopez C. Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities. IEEE Rev Biomed Eng 2024; 17:19-41. [PMID: 37943654 DOI: 10.1109/rbme.2023.3331297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
OBJECTIVE Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consistency in the methods, metrics, and data used to train and evaluate these algorithms. This manuscript provides consensus guidelines for machine learning practitioners in the field of diabetes, including best practice recommended approaches and warnings about pitfalls to avoid. METHODS Algorithmic approaches are reviewed and benefits of different algorithms are discussed including importance of clinical accuracy, explainability, interpretability, and personalization. We review the most common features used in machine learning applications in diabetes glucose control and provide an open-source library of functions for calculating features, as well as a framework for specifying data sets using data sheets. A review of current data sets available for training algorithms is provided as well as an online repository of data sources. SIGNIFICANCE These consensus guidelines are designed to improve performance and translatability of new machine learning algorithms developed in the field of diabetes for engineers and data scientists.
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Mosquera-Lopez C, Roquemen-Echeverri V, Tyler NS, Patton SR, Clements MA, Martin CK, Riddell MC, Gal RL, Gillingham M, Wilson LM, Castle JR, Jacobs PG. Combining uncertainty-aware predictive modeling and a bedtime Smart Snack intervention to prevent nocturnal hypoglycemia in people with type 1 diabetes on multiple daily injections. J Am Med Inform Assoc 2023; 31:109-118. [PMID: 37812784 PMCID: PMC10746320 DOI: 10.1093/jamia/ocad196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 09/25/2023] [Indexed: 10/11/2023] Open
Abstract
OBJECTIVE Nocturnal hypoglycemia is a known challenge for people with type 1 diabetes, especially for physically active individuals or those on multiple daily injections. We developed an evidential neural network (ENN) to predict at bedtime the probability and timing of nocturnal hypoglycemia (0-4 vs 4-8 h after bedtime) based on several glucose metrics and physical activity patterns. We utilized these predictions in silico to prescribe bedtime carbohydrates with a Smart Snack intervention specific to the predicted minimum nocturnal glucose and timing of nocturnal hypoglycemia. MATERIALS AND METHODS We leveraged free-living datasets collected from 366 individuals from the T1DEXI Study and Glooko. Inputs to the ENN used to model nocturnal hypoglycemia were derived from demographic information, continuous glucose monitoring, and physical activity data. We assessed the accuracy of the ENN using area under the receiver operating curve, and the clinical impact of the Smart Snack intervention through simulations. RESULTS The ENN achieved an area under the receiver operating curve of 0.80 and 0.71 to predict nocturnal hypoglycemic events during 0-4 and 4-8 h after bedtime, respectively, outperforming all evaluated baseline methods. Use of the Smart Snack intervention reduced probability of nocturnal hypoglycemia from 23.9 ± 14.1% to 14.0 ± 13.3% and duration from 7.4 ± 7.0% to 2.4 ± 3.3% in silico. DISCUSSION Our findings indicate that the ENN-based Smart Snack intervention has the potential to significantly reduce the frequency and duration of nocturnal hypoglycemic events. CONCLUSION A decision support system that combines prediction of minimum nocturnal glucose and proactive recommendations for bedtime carbohydrate intake might effectively prevent nocturnal hypoglycemia and reduce the burden of glycemic self-management.
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Affiliation(s)
- Clara Mosquera-Lopez
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, United States
| | - Valentina Roquemen-Echeverri
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, United States
| | - Nichole S Tyler
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, United States
| | - Susana R Patton
- Center for Healthcare Delivery Science, Nemours Children’s Health, Jacksonville, FL 32207, United States
| | - Mark A Clements
- Children’s Mercy Hospital, Kansas City, MO 64111, United States
- Glooko Inc., Palo Alto, CA 94301, United States
| | - Corby K Martin
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA 70808, United States
| | - Michael C Riddell
- Muscle Health Research Centre, York University, Toronto, ON M3J1P3, Canada
| | - Robin L Gal
- Jaeb Center for Health Research, Tampa, FL 33647, United States
| | - Melanie Gillingham
- Molecular and Medical Genetics, School of Medicine, Oregon Health & Science University, Portland, OR 97239, United States
| | - Leah M Wilson
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR 97239, United States
| | - Jessica R Castle
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR 97239, United States
| | - Peter G Jacobs
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, United States
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Liu K, Li L, Ma Y, Jiang J, Liu Z, Ye Z, Liu S, Pu C, Chen C, Wan Y. Machine Learning Models for Blood Glucose Level Prediction in Patients With Diabetes Mellitus: Systematic Review and Network Meta-Analysis. JMIR Med Inform 2023; 11:e47833. [PMID: 37983072 PMCID: PMC10696506 DOI: 10.2196/47833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 08/21/2023] [Accepted: 10/12/2023] [Indexed: 11/21/2023] Open
Abstract
BACKGROUND Machine learning (ML) models provide more choices to patients with diabetes mellitus (DM) to more properly manage blood glucose (BG) levels. However, because of numerous types of ML algorithms, choosing an appropriate model is vitally important. OBJECTIVE In a systematic review and network meta-analysis, this study aimed to comprehensively assess the performance of ML models in predicting BG levels. In addition, we assessed ML models used to detect and predict adverse BG (hypoglycemia) events by calculating pooled estimates of sensitivity and specificity. METHODS PubMed, Embase, Web of Science, and Institute of Electrical and Electronics Engineers Explore databases were systematically searched for studies on predicting BG levels and predicting or detecting adverse BG events using ML models, from inception to November 2022. Studies that assessed the performance of different ML models in predicting or detecting BG levels or adverse BG events of patients with DM were included. Studies with no derivation or performance metrics of ML models were excluded. The Quality Assessment of Diagnostic Accuracy Studies tool was applied to assess the quality of included studies. Primary outcomes were the relative ranking of ML models for predicting BG levels in different prediction horizons (PHs) and pooled estimates of the sensitivity and specificity of ML models in detecting or predicting adverse BG events. RESULTS In total, 46 eligible studies were included for meta-analysis. Regarding ML models for predicting BG levels, the means of the absolute root mean square error (RMSE) in a PH of 15, 30, 45, and 60 minutes were 18.88 (SD 19.71), 21.40 (SD 12.56), 21.27 (SD 5.17), and 30.01 (SD 7.23) mg/dL, respectively. The neural network model (NNM) showed the highest relative performance in different PHs. Furthermore, the pooled estimates of the positive likelihood ratio and the negative likelihood ratio of ML models were 8.3 (95% CI 5.7-12.0) and 0.31 (95% CI 0.22-0.44), respectively, for predicting hypoglycemia and 2.4 (95% CI 1.6-3.7) and 0.37 (95% CI 0.29-0.46), respectively, for detecting hypoglycemia. CONCLUSIONS Statistically significant high heterogeneity was detected in all subgroups, with different sources of heterogeneity. For predicting precise BG levels, the RMSE increases with a rise in the PH, and the NNM shows the highest relative performance among all the ML models. Meanwhile, current ML models have sufficient ability to predict adverse BG events, while their ability to detect adverse BG events needs to be enhanced. TRIAL REGISTRATION PROSPERO CRD42022375250; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=375250.
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Affiliation(s)
- Kui Liu
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Linyi Li
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Yifei Ma
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Jun Jiang
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Zhenhua Liu
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Zichen Ye
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Shuang Liu
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Chen Pu
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Changsheng Chen
- Department of Health Statistics, Air Force Medical University, Xi'an, Shaanxi, China
| | - Yi Wan
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
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Afentakis I, Unsworth R, Herrero P, Oliver N, Reddy M, Georgiou P. Development and Validation of Binary Classifiers to Predict Nocturnal Hypoglycemia in Adults With Type 1 Diabetes. J Diabetes Sci Technol 2023:19322968231185796. [PMID: 37434362 DOI: 10.1177/19322968231185796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/13/2023]
Abstract
BACKGROUND One of the biggest challenges for people with type 1 diabetes (T1D) using multiple daily injections (MDIs) is nocturnal hypoglycemia (NH). Recurrent NH can lead to serious complications; hence, prevention is of high importance. In this work, we develop and externally validate, device-agnostic Machine Learning (ML) models to provide bedtime decision support to people with T1D and minimize the risk of NH. METHODS We present the design and development of binary classifiers to predict NH (blood glucose levels occurring below 70 mg/dL). Using data collected from a 6-month study of 37 adult participants with T1D under free-living conditions, we extract daytime features from continuous glucose monitor (CGM) sensors, administered insulin, meal, and physical activity information. We use these features to train and test the performance of two ML algorithms: Random Forests (RF) and Support Vector Machines (SVMs). We further evaluate our model in an external population of 20 adults with T1D using MDI insulin therapy and wearing CGM and flash glucose monitoring sensors for two periods of eight weeks each. RESULTS At population-level, SVM outperforms RF algorithm with a receiver operating characteristic-area under curve (ROC-AUC) of 79.36% (95% CI: 76.86%, 81.86%). The proposed SVM model generalizes well in an unseen population (ROC-AUC = 77.06%), as well as between the two different glucose sensors (ROC-AUC = 77.74%). CONCLUSIONS Our model shows state-of-the-art performance, generalizability, and robustness in sensor devices from different manufacturers. We believe it is a potential viable approach to inform people with T1D about their risk of NH before it occurs.
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Affiliation(s)
- Ioannis Afentakis
- UK Research and Innovation Centre for Doctoral Training in Artificial Intelligence for Healthcare, Imperial College London, London, UK
- Department of Computing, Imperial College London, London, UK
| | | | - Pau Herrero
- Department of Electronic and Electrical Engineering, Imperial College London, London, UK
| | - Nick Oliver
- Department of Medicine, Imperial College London, London, UK
| | - Monika Reddy
- Department of Medicine, Imperial College London, London, UK
| | - Pantelis Georgiou
- Department of Electronic and Electrical Engineering, Imperial College London, London, UK
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Husain KH, Sarhan SF, AlKhalifa HKAA, Buhasan A, Moin ASM, Butler AE. Dementia in Diabetes: The Role of Hypoglycemia. Int J Mol Sci 2023; 24:9846. [PMID: 37372995 DOI: 10.3390/ijms24129846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 05/25/2023] [Accepted: 05/26/2023] [Indexed: 06/29/2023] Open
Abstract
Hypoglycemia, a common consequence of diabetes treatment, is associated with severe morbidity and mortality and has become a major barrier to intensifying antidiabetic therapy. Severe hypoglycemia, defined as abnormally low blood glucose requiring the assistance of another person, is associated with seizures and comas, but even mild hypoglycemia can cause troubling symptoms such as anxiety, palpitations, and confusion. Dementia generally refers to the loss of memory, language, problem-solving, and other cognitive functions, which can interfere with daily life, and there is growing evidence that diabetes is associated with an increased risk of both vascular and non-vascular dementia. Neuroglycopenia resulting from a hypoglycemic episode in diabetic patients can lead to the degeneration of brain cells, with a resultant cognitive decline, leading to dementia. In light of new evidence, a deeper understating of the relationship between hypoglycemia and dementia can help to inform and guide preventative strategies. In this review, we discuss the epidemiology of dementia among patients with diabetes, and the emerging mechanisms thought to underlie the association between hypoglycemia and dementia. Furthermore, we discuss the risks of various pharmacological therapies, emerging therapies to combat hypoglycemia-induced dementia, as well as risk minimization strategies.
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Affiliation(s)
- Khaled Hameed Husain
- School of Medicine, Royal College of Surgeons in Ireland, Busaiteen, Adliya 15503, Bahrain
| | - Saud Faisal Sarhan
- School of Medicine, Royal College of Surgeons in Ireland, Busaiteen, Adliya 15503, Bahrain
| | | | - Asal Buhasan
- School of Medicine, Royal College of Surgeons in Ireland, Busaiteen, Adliya 15503, Bahrain
| | - Abu Saleh Md Moin
- Research Department, Royal College of Surgeons in Ireland, Busaiteen, Adliya 15503, Bahrain
| | - Alexandra E Butler
- Research Department, Royal College of Surgeons in Ireland, Busaiteen, Adliya 15503, Bahrain
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Zhang L, Yang L, Zhou Z. Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice. Front Public Health 2023; 11:1044059. [PMID: 36778566 PMCID: PMC9910805 DOI: 10.3389/fpubh.2023.1044059] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 01/10/2023] [Indexed: 01/27/2023] Open
Abstract
Background and objective Hypoglycemia is a key barrier to achieving optimal glycemic control in people with diabetes, which has been proven to cause a set of deleterious outcomes, such as impaired cognition, increased cardiovascular disease, and mortality. Hypoglycemia prediction has come to play a role in diabetes management as big data analysis and machine learning (ML) approaches have become increasingly prevalent in recent years. As a result, a review is needed to summarize the existing prediction algorithms and models to guide better clinical practice in hypoglycemia prevention. Materials and methods PubMed, EMBASE, and the Cochrane Library were searched for relevant studies published between 1 January 2015 and 8 December 2022. Five hypoglycemia prediction aspects were covered: real-time hypoglycemia, mild and severe hypoglycemia, nocturnal hypoglycemia, inpatient hypoglycemia, and other hypoglycemia (postprandial, exercise-related). Results From the 5,042 records retrieved, we included 79 studies in our analysis. Two major categories of prediction models are identified by an overview of the chosen studies: simple or logistic regression models based on clinical data and data-based ML models (continuous glucose monitoring data is most commonly used). Models utilizing clinical data have identified a variety of risk factors that can lead to hypoglycemic events. Data-driven models based on various techniques such as neural networks, autoregressive, ensemble learning, supervised learning, and mathematical formulas have also revealed suggestive features in cases of hypoglycemia prediction. Conclusion In this study, we looked deep into the currently established hypoglycemia prediction models and identified hypoglycemia risk factors from various perspectives, which may provide readers with a better understanding of future trends in this topic.
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Afsaneh E, Sharifdini A, Ghazzaghi H, Ghobadi MZ. Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review. Diabetol Metab Syndr 2022; 14:196. [PMID: 36572938 PMCID: PMC9793536 DOI: 10.1186/s13098-022-00969-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 12/16/2022] [Indexed: 12/28/2022] Open
Abstract
Diabetes as a metabolic illness can be characterized by increased amounts of blood glucose. This abnormal increase can lead to critical detriment to the other organs such as the kidneys, eyes, heart, nerves, and blood vessels. Therefore, its prediction, prognosis, and management are essential to prevent harmful effects and also recommend more useful treatments. For these goals, machine learning algorithms have found considerable attention and have been developed successfully. This review surveys the recently proposed machine learning (ML) and deep learning (DL) models for the objectives mentioned earlier. The reported results disclose that the ML and DL algorithms are promising approaches for controlling blood glucose and diabetes. However, they should be improved and employed in large datasets to affirm their applicability.
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Castle JR, Wilson LM, Tyler NS, Espinoza AZ, Mosquera-Lopez CM, Kushner T, Young GM, Pinsonault J, Dodier RH, Hilts WW, Oganessian SM, Branigan DL, Gabo VB, Eom JH, Ramsey K, Youssef JE, Cafazzo JA, Winters-Stone K, Jacobs PG. Assessment of a Decision Support System for Adults with Type 1 Diabetes on Multiple Daily Insulin Injections. Diabetes Technol Ther 2022; 24:892-897. [PMID: 35920839 PMCID: PMC9700374 DOI: 10.1089/dia.2022.0252] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Introduction: DailyDose is a decision support system designed to provide real-time dosing advice and weekly insulin dose adjustments for adults living with type 1 diabetes using multiple daily insulin injections. Materials and Methods: Twenty-five adults were enrolled in this single-arm study. All participants used Dexcom G6 for continuous glucose monitoring, InPen for short-acting insulin doses, and Clipsulin to track long-acting insulin doses. Participants used DailyDose on an iPhone for 8 weeks. The primary endpoint was % time in range (TIR) comparing the 2-week baseline to the final 2-week period of DailyDose use. Results: There were no significant differences between TIR or other glycemic metrics between the baseline period compared to final 2-week period of DailyDose use. TIR significantly improved by 6.3% when more than half of recommendations were accepted and followed compared with 50% or fewer recommendations (95% CI 2.5%-10.1%, P = 0.001). Conclusions: Use of DailyDose did not improve glycemic outcomes compared to the baseline period. In a post hoc analysis, accepting and following recommendations from DailyDose was associated with improved TIR. Clinical Trial Registration Number: NCT04428645.
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Affiliation(s)
- Jessica R. Castle
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Leah M. Wilson
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Nichole S. Tyler
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Alejandro Z. Espinoza
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Clara M. Mosquera-Lopez
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Taisa Kushner
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Gavin M. Young
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Joseph Pinsonault
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Robert H. Dodier
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Wade W. Hilts
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Sos M. Oganessian
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Deborah L. Branigan
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Virginia B. Gabo
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Jae H. Eom
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Katrina Ramsey
- Biostatistics & Design Program, Oregon Health & Science University, Portland, Oregon, USA
| | - Joseph El Youssef
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Joseph A. Cafazzo
- Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, Canada
- Dalla Lana School of Public Health, Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
| | - Kerri Winters-Stone
- Division of Oncological Sciences, Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA
| | - Peter G. Jacobs
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
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Parcerisas A, Contreras I, Delecourt A, Bertachi A, Beneyto A, Conget I, Viñals C, Giménez M, Vehi J. A Machine Learning Approach to Minimize Nocturnal Hypoglycemic Events in Type 1 Diabetic Patients under Multiple Doses of Insulin. Sensors (Basel) 2022; 22:s22041665. [PMID: 35214566 PMCID: PMC8876195 DOI: 10.3390/s22041665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 02/10/2022] [Accepted: 02/19/2022] [Indexed: 11/16/2022]
Abstract
Nocturnal hypoglycemia (NH) is one of the most challenging events for multiple dose insulin therapy (MDI) in people with type 1 diabetes (T1D). The goal of this study is to design a method to reduce the incidence of NH in people with T1D under MDI therapy, providing a decision-support system and improving confidence toward self-management of the disease considering the dataset used by Bertachi et al. Different machine learning (ML) algorithms, data sources, optimization metrics and mitigation measures to predict and avoid NH events have been studied. In addition, we have designed population and personalized models and studied the generalizability of the models and the influence of physical activity (PA) on them. Obtaining 30 g of rescue carbohydrates (CHO) is the optimal value for preventing NH, so it can be asserted that this is the value with which the time under 70 mg/dL decreases the most, with almost a 35% reduction, while increasing the time in the target range by 1.3%. This study supports the feasibility of using ML techniques to address the prediction of NH in patients with T1D under MDI therapy, using continuous glucose monitoring (CGM) and a PA tracker. The results obtained prove that BG predictions can not only be critical in achieving safer diabetes management, but also assist physicians and patients to make better and safer decisions regarding insulin therapy and their day-to-day lives.
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Affiliation(s)
- Adrià Parcerisas
- Institut d’Informàtica i Aplicacions, Universitat de Girona, 17003 Girona, Spain; (A.P.); (I.C.); (A.D.); (A.B.)
| | - Ivan Contreras
- Institut d’Informàtica i Aplicacions, Universitat de Girona, 17003 Girona, Spain; (A.P.); (I.C.); (A.D.); (A.B.)
| | - Alexia Delecourt
- Institut d’Informàtica i Aplicacions, Universitat de Girona, 17003 Girona, Spain; (A.P.); (I.C.); (A.D.); (A.B.)
| | - Arthur Bertachi
- Campus Guarapuava, Federal University of Technology—Paraná (UTFPR), Guarapuava 85053-525, Brazil;
| | - Aleix Beneyto
- Institut d’Informàtica i Aplicacions, Universitat de Girona, 17003 Girona, Spain; (A.P.); (I.C.); (A.D.); (A.B.)
| | - Ignacio Conget
- Endocrinology and Diabetes Unit, Hospital Clínic, 08036 Barcelona, Spain; (I.C.); (C.V.); (M.G.)
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), 28029 Madrid, Spain
- Institut d’Investigacions Biomèdiques August Pi i Sunyer, 08036 Barcelona, Spain
| | - Clara Viñals
- Endocrinology and Diabetes Unit, Hospital Clínic, 08036 Barcelona, Spain; (I.C.); (C.V.); (M.G.)
| | - Marga Giménez
- Endocrinology and Diabetes Unit, Hospital Clínic, 08036 Barcelona, Spain; (I.C.); (C.V.); (M.G.)
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), 28029 Madrid, Spain
- Institut d’Investigacions Biomèdiques August Pi i Sunyer, 08036 Barcelona, Spain
| | - Josep Vehi
- Institut d’Informàtica i Aplicacions, Universitat de Girona, 17003 Girona, Spain; (A.P.); (I.C.); (A.D.); (A.B.)
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), 28029 Madrid, Spain
- Correspondence:
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12
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Salah NY, Abido AY, Rashed HR. Relationship of glycaemic derangement using continuous glucose monitoring system with sleep pattern among children with type 1 diabetes. Diabetes Metab Res Rev 2021; 37:e3407. [PMID: 32935448 DOI: 10.1002/dmrr.3407] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 07/25/2020] [Accepted: 08/24/2020] [Indexed: 12/23/2022]
Abstract
BACKGROUND Glycaemic derangement has been linked to sleep disruption. However, the impact of glycaemic derangement on sleep pattern among children with type 1 diabetes (C-T1D) remains unraveled. AIM To assess the effect of nocturnal hyperglycaemia and clinically significant (CS) hypoglycaemia on sleep pattern among C-T1D. METHODOLOGY Thirty C-T1D were compared to 30 age and sex matched healthy siblings. Patients having other organ disease that might cause sleep disorders or on medications causing sleep disturbance were excluded. History included diabetes-duration, type and dose of insulin therapy, chronic diabetic-complications, and manifestations of sleep disorders. Epworth Sleepiness Scale-Child Adolescent was used. Continuous glucose monitoring system (CGMS) and overnight polysomnography were done and analysed. RESULTS C-T1D had significantly lower sleep efficiency and significantly higher arousal index (AI), periodic limb movement index and apnoea-hypopnoea index compared to controls. Moreover, they had significantly longer sleep-onset latency, light sleep percentage, and shorter rapid eye movement percentage than controls. According to nocturnal CGMS readings, 15 C-T1D had nocturnal hyperglycaemia (50%), six experienced CS hypoglycaemia (20%), two had level-1 hypoglycaemia (6.7%), and seven were within the normoglycaemic range (23.3%). C-T1D experiencing nocturnal CS hypoglycaemia had significantly higher stage 3 sleep (P = 0.004) than controls. On the other hand, C- T1D experiencing nocturnal hyperglycaemia had significantly higher sleep onset latency (P = 0.013), light sleep percentage (P < 0.001), and AI (P < 0.001) than controls. Nocturnal CS hypoglycaemia was positively correlated to deep sleep duration, while hyperglycaemia was correlated to number of awakenings, sleep-onset latency, and light sleep duration. CONCLUSION In children with T1D CS hypoglycaemia is associated with sleep deepening, while hyperglycaemia is associated with increased light sleep, sleep onset latency.
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Affiliation(s)
- Nouran Y Salah
- Department of Pediatrics, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Amal Y Abido
- General Practitioner, Health Insurance Hospital, Giza, Egypt
| | - Hebatallah R Rashed
- Department of Neurology, Faculty of Medicine, Ain Shams University, Cairo, Egypt
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Kodama S, Fujihara K, Shiozaki H, Horikawa C, Yamada MH, Sato T, Yaguchi Y, Yamamoto M, Kitazawa M, Iwanaga M, Matsubayashi Y, Sone H. Ability of Current Machine Learning Algorithms to Predict and Detect Hypoglycemia in Patients With Diabetes Mellitus: Meta-analysis. JMIR Diabetes 2021; 6:e22458. [PMID: 33512324 PMCID: PMC7880810 DOI: 10.2196/22458] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 11/09/2020] [Accepted: 12/07/2020] [Indexed: 12/12/2022] Open
Abstract
Background Machine learning (ML) algorithms have been widely introduced to diabetes research including those for the identification of hypoglycemia. Objective The objective of this meta-analysis is to assess the current ability of ML algorithms to detect hypoglycemia (ie, alert to hypoglycemia coinciding with its symptoms) or predict hypoglycemia (ie, alert to hypoglycemia before its symptoms have occurred). Methods Electronic literature searches (from January 1, 1950, to September 14, 2020) were conducted using the Dialog platform that covers 96 databases of peer-reviewed literature. Included studies had to train the ML algorithm in order to build a model to detect or predict hypoglycemia and test its performance. The set of 2 × 2 data (ie, number of true positives, false positives, true negatives, and false negatives) was pooled with a hierarchical summary receiver operating characteristic model. Results A total of 33 studies (14 studies for detecting hypoglycemia and 19 studies for predicting hypoglycemia) were eligible. For detection of hypoglycemia, pooled estimates (95% CI) of sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were 0.79 (0.75-0.83), 0.80 (0.64-0.91), 8.05 (4.79-13.51), and 0.18 (0.12-0.27), respectively. For prediction of hypoglycemia, pooled estimates (95% CI) were 0.80 (0.72-0.86) for sensitivity, 0.92 (0.87-0.96) for specificity, 10.42 (5.82-18.65) for PLR, and 0.22 (0.15-0.31) for NLR. Conclusions Current ML algorithms have insufficient ability to detect ongoing hypoglycemia and considerate ability to predict impeding hypoglycemia in patients with diabetes mellitus using hypoglycemic drugs with regard to diagnostic tests in accordance with the Users’ Guide to Medical Literature (PLR should be ≥5 and NLR should be ≤0.2 for moderate reliability). However, it should be emphasized that the clinical applicability of these ML algorithms should be evaluated according to patients’ risk profiles such as for hypoglycemia and its associated complications (eg, arrhythmia, neuroglycopenia) as well as the average ability of the ML algorithms. Continued research is required to develop more accurate ML algorithms than those that currently exist and to enhance the feasibility of applying ML in clinical settings. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42020163682; http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42020163682
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Affiliation(s)
- Satoru Kodama
- Department of Prevention of Noncommunicable Diseases and Promotion of Health Checkup, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Kazuya Fujihara
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Haruka Shiozaki
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Chika Horikawa
- Department of Health and Nutrition, Faculty of Human Life Studies, University of Niigata Prefecture, Niigata, Japan
| | - Mayuko Harada Yamada
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Takaaki Sato
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Yuta Yaguchi
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Masahiko Yamamoto
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Masaru Kitazawa
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Midori Iwanaga
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Yasuhiro Matsubayashi
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Hirohito Sone
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
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Mujahid O, Contreras I, Vehi J. Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges. Sensors (Basel) 2021; 21:E546. [PMID: 33466659 PMCID: PMC7828835 DOI: 10.3390/s21020546] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/08/2021] [Accepted: 01/12/2021] [Indexed: 12/11/2022]
Abstract
(1) Background: the use of machine learning techniques for the purpose of anticipating hypoglycemia has increased considerably in the past few years. Hypoglycemia is the drop in blood glucose below critical levels in diabetic patients. This may cause loss of cognitive ability, seizures, and in extreme cases, death. In almost half of all the severe cases, hypoglycemia arrives unannounced and is essentially asymptomatic. The inability of a diabetic patient to anticipate and intervene the occurrence of a hypoglycemic event often results in crisis. Hence, the prediction of hypoglycemia is a vital step in improving the life quality of a diabetic patient. The objective of this paper is to review work performed in the domain of hypoglycemia prediction by using machine learning and also to explore the latest trends and challenges that the researchers face in this area; (2) Methods: literature obtained from PubMed and Google Scholar was reviewed. Manuscripts from the last five years were searched for this purpose. A total of 903 papers were initially selected of which 57 papers were eventually shortlisted for detailed review; (3) Results: a thorough dissection of the shortlisted manuscripts provided an interesting split between the works based on two categories: hypoglycemia prediction and hypoglycemia detection. The entire review was carried out keeping this categorical distinction in perspective while providing a thorough overview of the machine learning approaches used to anticipate hypoglycemia, the type of training data, and the prediction horizon.
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Affiliation(s)
- Omer Mujahid
- Model Identification and Control Laboratory, Institut d’Informatica i Applicacions, Universitat de Girona, 17003 Girona, Spain; (O.M.); (I.C.)
| | - Ivan Contreras
- Model Identification and Control Laboratory, Institut d’Informatica i Applicacions, Universitat de Girona, 17003 Girona, Spain; (O.M.); (I.C.)
| | - Josep Vehi
- Model Identification and Control Laboratory, Institut d’Informatica i Applicacions, Universitat de Girona, 17003 Girona, Spain; (O.M.); (I.C.)
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), 17003 Girona, Spain
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Affiliation(s)
- Feng Qian
- Department of Health Policy, Management, and Behavior, School of Public Health, University at Albany-State University of New York, Rensselaer, NY, USA
- Feng Qian, MD, PhD, MBA, One University Place, GEC Rm169, Rensselaer, NY 12144-3445, USA.
| | - Patrick J. Schumacher
- Department of Health Policy, Management, and Behavior, School of Public Health, University at Albany-State University of New York, Rensselaer, NY, USA
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Tyler NS, Jacobs PG. Artificial Intelligence in Decision Support Systems for Type 1 Diabetes. Sensors (Basel) 2020; 20:E3214. [PMID: 32517068 PMCID: PMC7308977 DOI: 10.3390/s20113214] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 05/29/2020] [Accepted: 06/02/2020] [Indexed: 12/16/2022]
Abstract
Type 1 diabetes (T1D) is a chronic health condition resulting from pancreatic beta cell dysfunction and insulin depletion. While automated insulin delivery systems are now available, many people choose to manage insulin delivery manually through insulin pumps or through multiple daily injections. Frequent insulin titrations are needed to adequately manage glucose, however, provider adjustments are typically made every several months. Recent automated decision support systems incorporate artificial intelligence algorithms to deliver personalized recommendations regarding insulin doses and daily behaviors. This paper presents a comprehensive review of computational and artificial intelligence-based decision support systems to manage T1D. Articles were obtained from PubMed, IEEE Xplore, and ScienceDirect databases. No time period restrictions were imposed on the search. After removing off-topic articles and duplicates, 562 articles were left to review. Of those articles, we identified 61 articles for comprehensive review based on algorithm evaluation using real-world human data, in silico trials, or clinical studies. We grouped decision support systems into general categories of (1) those which recommend adjustments to insulin and (2) those which predict and help avoid hypoglycemia. We review the artificial intelligence methods used for each type of decision support system, and discuss the performance and potential applications of these systems.
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Affiliation(s)
| | - Peter G. Jacobs
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA;
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