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Tahir F, Farhan M. Exploring the progress of artificial intelligence in managing type 2 diabetes mellitus: a comprehensive review of present innovations and anticipated challenges ahead. FRONTIERS IN CLINICAL DIABETES AND HEALTHCARE 2023; 4:1316111. [PMID: 38161783 PMCID: PMC10757318 DOI: 10.3389/fcdhc.2023.1316111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 11/24/2023] [Indexed: 01/03/2024]
Abstract
A significant worldwide health issue, Type 2 Diabetes Mellitus (T2DM) calls for creative solutions. This in-depth review examines the growing severity of T2DM and the requirement for individualized management approaches. It explores the use of artificial intelligence (AI) in the treatment of diabetes, highlighting its potential for diagnosis, customized treatment plans, and patient self-management. The paper highlights the roles played by AI applications such as expert systems, machine learning algorithms, and deep learning approaches in the identification of retinopathy, the interpretation of clinical guidelines, and prediction models. Examined are difficulties with individualized diabetes treatment, including complex technological issues and patient involvement. The review highlights the revolutionary potential of AI in the management of diabetes and calls for a balanced strategy in which AI supports clinical knowledge. It is crucial to pay attention to ethical issues, data privacy, and joint research initiatives.
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Affiliation(s)
- Farwa Tahir
- Department of Pharmacy, Rashid Latif Medical Complex, Lahore, Pakistan
| | - Muhammad Farhan
- Department of Pharmacy, University of Lahore, Islamabad, Pakistan
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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] [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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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] [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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Wadghiri MZ, Idri A, El Idrissi T, Hakkoum H. Ensemble blood glucose prediction in diabetes mellitus: A review. Comput Biol Med 2022; 147:105674. [PMID: 35716436 DOI: 10.1016/j.compbiomed.2022.105674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 04/28/2022] [Accepted: 05/25/2022] [Indexed: 11/03/2022]
Abstract
Considering the complexity of blood glucose dynamics, the adoption of a single model to predict blood glucose level does not always capture the inter- and intra-patients' context changes. Ensembles are a set of machine learning techniques combining multiple single learners to find a better variance/bias trade-off and hence improve the prediction accuracy. The present paper aims to review the state of the art in predicting blood glucose using ensemble methods with regard to 8 criteria: publication year and sources, datasets used to train/evaluate the models, types of ensembles used, single learners involved to construct ensembles, combination schemes used to aggregate the base learners, metrics and validation methods adopted to assess the performance of ensembles, reported overall performance of the predictors and accuracy comparison of ensemble techniques with single models. A systematic literature review has been conducted in order to analyze and synthetize primary studies published between 2000 and 2020 in six digital libraries. A total of 32 primary papers were selected and reviewed with regard to eight review questions. The results show that ensembles have gained wider interest during the last years and improved in general the performance compared with other single models. However, multiple gaps have been identified concerning the ensembles construction process and the performance metrics used. Several recommendations have been made in this regard to design accurate ensembles for blood glucose level prediction.
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Affiliation(s)
- M Z Wadghiri
- Software Project Management Research Team, ENSIAS, Mohammed V University in Rabat, Morocco
| | - A Idri
- Software Project Management Research Team, ENSIAS, Mohammed V University in Rabat, Morocco; MSDA, Mohammed VI Polytechnic University, Benguerir, Morocco.
| | - Touria El Idrissi
- Software Project Management Research Team, ENSIAS, Mohammed V University in Rabat, Morocco
| | - Hajar Hakkoum
- Software Project Management Research Team, ENSIAS, Mohammed V University in Rabat, Morocco
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Zhang P, Fonnesbeck C, Schmidt DC, White J, Kleinberg S, Mulvaney SA. Using Momentary Assessment and Machine Learning to Identify Barriers to Self-management in Type 1 Diabetes: Observational Study. JMIR Mhealth Uhealth 2022; 10:e21959. [PMID: 35238791 PMCID: PMC8931646 DOI: 10.2196/21959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 07/16/2021] [Accepted: 12/15/2021] [Indexed: 11/13/2022] Open
Abstract
Background For adolescents living with type 1 diabetes (T1D), completion of multiple daily self-management tasks, such as monitoring blood glucose and administering insulin, can be challenging because of psychosocial and contextual barriers. These barriers are hard to assess accurately and specifically by using traditional retrospective recall. Ecological momentary assessment (EMA) uses mobile technologies to assess the contexts, subjective experiences, and psychosocial processes that surround self-management decision-making in daily life. However, the rich data generated via EMA have not been frequently examined in T1D or integrated with machine learning analytic approaches. Objective The goal of this study is to develop a machine learning algorithm to predict the risk of missed self-management in young adults with T1D. To achieve this goal, we train and compare a number of machine learning models through a learned filtering architecture to explore the extent to which EMA data were associated with the completion of two self-management behaviors: mealtime self-monitoring of blood glucose (SMBG) and insulin administration. Methods We analyzed data from a randomized controlled pilot study using machine learning–based filtering architecture to investigate whether novel information related to contextual, psychosocial, and time-related factors (ie, time of day) relate to self-management. We combined EMA-collected contextual and insulin variables via the MyDay mobile app with Bluetooth blood glucose data to construct machine learning classifiers that predicted the 2 self-management behaviors of interest. Results With 1231 day-level SMBG frequency counts for 45 participants, demographic variables and time-related variables were able to predict whether daily SMBG was below the clinical threshold of 4 times a day. Using the 1869 data points derived from app-based EMA data of 31 participants, our learned filtering architecture method was able to infer nonadherence events with high accuracy and precision. Although the recall score is low, there is high confidence that the nonadherence events identified by the model are truly nonadherent. Conclusions Combining EMA data with machine learning methods showed promise in the relationship with risk for nonadherence. The next steps include collecting larger data sets that would more effectively power a classifier that can be deployed to infer individual behavior. Improvements in individual self-management insights, behavioral risk predictions, enhanced clinical decision-making, and just-in-time patient support in diabetes could result from this type of approach.
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Affiliation(s)
- Peng Zhang
- Department of Computer Science, School of Engineering, Vanderbilt University, Nashville, TN, United States
- Data Science Institute, Vanderbilt University, Nashville, TN, United States
| | | | - Douglas C Schmidt
- Department of Computer Science, School of Engineering, Vanderbilt University, Nashville, TN, United States
- Data Science Institute, Vanderbilt University, Nashville, TN, United States
| | - Jules White
- Department of Computer Science, School of Engineering, Vanderbilt University, Nashville, TN, United States
| | - Samantha Kleinberg
- Department of Computer Science, Stevens Institute of Technology, Hoboken, NJ, United States
| | - Shelagh A Mulvaney
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
- School of Nursing, Vanderbilt University, Nashville, TN, United States
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Machine Learning and Smart Devices for Diabetes Management: Systematic Review. SENSORS 2022; 22:s22051843. [PMID: 35270989 PMCID: PMC8915068 DOI: 10.3390/s22051843] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/05/2022] [Accepted: 02/18/2022] [Indexed: 01/27/2023]
Abstract
(1) Background: The use of smart devices to better manage diabetes has increased significantly in recent years. These technologies have been introduced in order to make life easier for patients with diabetes by allowing better control of the stability of blood sugar levels and anticipating the occurrence of dangerous events (hypo/hyperglycemia), etc. That being said, the main objectives of the self-management of diabetes is to improve the lifestyle and life quality of patients with diabetes; (2) Methods: We performed a systematic review based on articles that focus on the use of smart devices for the monitoring and better management of diabetes. The search was focused on keywords related to the topic, such as “Diabetes”, “Technology”, “Self-management”, “Artificial Intelligence”, etc. This was performed using databases, such as Scopus, Google Scholar, and PubMed; (3) Results: A total of 89 studies, published between 2011 and 2021, were included. The majority of the selected research aims to solve a diabetes management problem (e.g., blood glucose prediction, early detection of risk events, and the automatic adjustment of insulin doses, etc.). In these studies, wearable devices were used in combination with artificial intelligence (AI) techniques; (4) Conclusions: Wearable devices have attracted a great deal of scientific interest in the field of healthcare for people with chronic conditions, such as diabetes. They are capable of assisting in the management of diabetes, as well as preventing complications associated with this condition. Furthermore, the usage of these devices has improved illness management and quality of life.
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Pollock RF, Norrbacka K, Boye KS, Osumili B, Valentine WJ. The PRIME Type 2 Diabetes Model: a novel, patient-level model for estimating long-term clinical and cost outcomes in patients with type 2 diabetes mellitus. J Med Econ 2022; 25:393-402. [PMID: 35105267 DOI: 10.1080/13696998.2022.2035132] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
BACKGROUND AND AIMS The growing burden of diabetes mellitus and recent progress in understanding cardiovascular outcomes for type 2 diabetes (T2D) patients continue to make the disease a priority for healthcare decision-makers around the world. Our objective was to develop a new, product-independent model capable of projecting long-term clinical and cost outcomes for populations with T2D to support health economic evaluation. METHODS Following a systematic literature review to identify longitudinal study data, existing T2D models and risk formulae for T2D populations, a model was developed (the PRIME Type 2 Diabetes Model [PRIME T2D Model]) in line with good practice guidelines to simulate disease progression, diabetes-related complications and mortality. The model runs as a patient-level simulation and is capable of simulating treatment algorithms and risk factor progression, and projecting the cumulative incidence of macrovascular and microvascular complications as well as hypoglycemic events. The PRIME T2D Model can report clinical outcomes, quality-adjusted life expectancy, direct and indirect costs, along with standard measures of cost-effectiveness and is capable of probabilistic sensitivity analysis. Several approaches novel to T2D modeling were utilized, such as combining risk formulae using a weighted model averaging approach that takes into account patient characteristics to evaluate complication risk. RESULTS Validation analyses comparing modeled outcomes with published studies demonstrated that the PRIME T2D Model projects long-term patient outcomes consistent with those reported for a number of long-term studies, including cardiovascular outcomes trials. All root mean squared deviation (RMSD) values for internal validations (against published studies used to develop the model) were 1.1% or less and all external validation RMSDs were 3.7% or less. CONCLUSIONS The PRIME T2D Model is a product-independent analysis tool that is available online and offers new approaches to long-standing challenges in diabetes modeling and may become a useful tool for informing healthcare policy.HIGHLIGHTSThe PRIME Type 2 Diabetes (T2D) Model is a new, product-independent simulation model.The model offers new approaches to long-standing challenges in diabetes modeling.PRIME T2D Model projects outcomes consistent with those from clinical trials.The model is designed to be a useful tool for informing healthcare policy in T2D.
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Affiliation(s)
- Richard F Pollock
- Health Economics and Outcomes Research, Covalence Research Ltd, London, UK
| | | | - Kristina S Boye
- Global Patient Outcomes and Real World Evidence, Eli Lilly and Company, Indianapolis, USA
| | | | - William J Valentine
- Health Economics, Ossian Health Economics and Communications, Basel, Switzerland
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A predictive model incorporating the change detection and Winsorization methods for alerting hypoglycemia and hyperglycemia. Med Biol Eng Comput 2021; 59:2311-2324. [PMID: 34591245 DOI: 10.1007/s11517-021-02433-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 08/18/2021] [Indexed: 10/20/2022]
Abstract
This paper focuses on establishing an effective predictive model to quickly and accurately alert hypoglycemia and hyperglycemia for helping control blood glucose levels of people with diabetes. In general, a good predictive model is established on the features of data. Inspired by this, we first analyze the characteristics of continuous glucose monitoring (CGM) data by the equality of variances test and outlier detection, which show time-varying fluctuations and jump points in CGM data. Therefore, we incorporate the change detection method and the Winsorization method into the predictive model based on the autoregressive moving average (ARMA) model and the recursive least squares (RLS) method to fit the above characteristics. To the best of our knowledge, the proposed method is the first attempt to give a solution for matching the time-varying fluctuations and jump points of CGM data simultaneously. A case study using CGM data is given to validate the effectiveness of the proposed method under 30-min-ahead prediction. The results show that the proposed method can improve the true alarm ratio of hypoglycemia and hyperglycemia from 0.7983 to 0.8783, and lengthen the average advance detection time of hypoglycemia and hyperglycemia from 19.77 to 22.64 min.
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Diouri O, Cigler M, Vettoretti M, Mader JK, Choudhary P, Renard E. Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments. Diabetes Metab Res Rev 2021; 37:e3449. [PMID: 33763974 PMCID: PMC8519027 DOI: 10.1002/dmrr.3449] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 12/08/2020] [Accepted: 01/28/2021] [Indexed: 02/06/2023]
Abstract
The main objective of diabetes control is to correct hyperglycaemia while avoiding hypoglycaemia, especially in insulin-treated patients. Fear of hypoglycaemia is a hurdle to effective correction of hyperglycaemia because it promotes under-dosing of insulin. Strategies to minimise hypoglycaemia include education and training for improved hypoglycaemia awareness and the development of technologies to allow their early detection and thus minimise their occurrence. Patients with impaired hypoglycaemia awareness would benefit the most from these technologies. The purpose of this systematic review is to review currently available or in-development technologies that support detection of hypoglycaemia or hypoglycaemia risk, and identify gaps in the research. Nanomaterial use in sensors is a promising strategy to increase the accuracy of continuous glucose monitoring devices for low glucose values. Hypoglycaemia is associated with changes on vital signs, so electrocardiogram and encephalogram could also be used to detect hypoglycaemia. Accuracy improvements through multivariable measures can make already marketed galvanic skin response devices a good noninvasive alternative. Breath volatile organic compounds can be detected by dogs and devices and alert patients at hypoglycaemia onset, while near-infrared spectroscopy can also be used as a hypoglycaemia alarms. Finally, one of the main directions of research are deep learning algorithms to analyse continuous glucose monitoring data and provide earlier and more accurate prediction of hypoglycaemia. Current developments for early identification of hypoglycaemia risk combine improvements of available 'needle-type' enzymatic glucose sensors and noninvasive alternatives. Patient usability will be essential to demonstrate to allow their implementation for daily use in diabetes management.
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Affiliation(s)
- Omar Diouri
- Department of Endocrinology, Diabetes, NutritionMontpellier University HospitalMontpellierFrance
- Department of PhysiologyInstitute of Functional Genomics, CNRS, INSERMUniversity of MontpellierMontpellierFrance
| | - Monika Cigler
- Division of Endocrinology and DiabetologyDepartment of Internal MedicineMedical University of GrazGrazAustria
| | | | - Julia K. Mader
- Division of Endocrinology and DiabetologyDepartment of Internal MedicineMedical University of GrazGrazAustria
| | - Pratik Choudhary
- Department of Diabetes and Nutritional SciencesKing's College LondonLondonUK
- Diabetes Research CentreUniversity of LeicesterLeicesterUK
| | - Eric Renard
- Department of Endocrinology, Diabetes, NutritionMontpellier University HospitalMontpellierFrance
- Department of PhysiologyInstitute of Functional Genomics, CNRS, INSERMUniversity of MontpellierMontpellierFrance
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Dave D, DeSalvo DJ, Haridas B, McKay S, Shenoy A, Koh CJ, Lawley M, Erraguntla M. Feature-Based Machine Learning Model for Real-Time Hypoglycemia Prediction. J Diabetes Sci Technol 2021; 15:842-855. [PMID: 32476492 PMCID: PMC8258517 DOI: 10.1177/1932296820922622] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Hypoglycemia is a serious health concern in youth with type 1 diabetes (T1D). Real-time data from continuous glucose monitoring (CGM) can be used to predict hypoglycemic risk, allowing patients to take timely intervention measures. METHODS A machine learning model is developed for probabilistic prediction of hypoglycemia (<70 mg/dL) in 30- and 60-minute time horizons based on CGM datasets obtained from 112 patients over a range of 90 days consisting of over 1.6 million CGM values under normal living conditions. A comprehensive set of features relevant for hypoglycemia are developed and a parsimonious subset with most influence on predicting hypoglycemic risk is identified. Model performance is evaluated both with and without contextual information on insulin and carbohydrate intake. RESULTS The model predicted hypoglycemia with >91% sensitivity for 30- and 60-minute prediction horizons while maintaining specificity >90%. Inclusion of insulin and carbohydrate data yielded performance improvement for 60-minute but not for 30-minute predictions. Model performance was highest for nocturnal hypoglycemia (~95% sensitivity). Shortterm (less than one hour) and medium-term (one to four hours) features for good prediction performance are identified. CONCLUSIONS Innovative feature identification facilitated high performance for hypoglycemia risk prediction in pediatric youth with T1D. Timely alerts of impending hypoglycemia may enable proactive measures to avoid severe hypoglycemia and achieve optimal glycemic control. The model will be deployed on a patient-facing smartphone application in an upcoming pilot study.
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Affiliation(s)
- Darpit Dave
- Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA
| | - Daniel J. DeSalvo
- Baylor College of Medicine, Houston, TX, USA
- Texas Children’s Hospital, Houston, TX, USA
| | - Balakrishna Haridas
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA
| | - Siripoom McKay
- Baylor College of Medicine, Houston, TX, USA
- Texas Children’s Hospital, Houston, TX, USA
| | | | - Chester J. Koh
- Baylor College of Medicine, Houston, TX, USA
- Texas Children’s Hospital, Houston, TX, USA
| | - Mark Lawley
- Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA
| | - Madhav Erraguntla
- Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA
- Madhav Erraguntla, PhD, Department of Industrial and Systems Engineering, Texas A&M University, 4021 Emerging Technology Building, College Station, TX 77843, USA.
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Dave D, Erraguntla M, Lawley M, DeSalvo D, Haridas B, McKay S, Koh C. Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study. JMIR Diabetes 2021; 6:e26909. [PMID: 33913816 PMCID: PMC8120423 DOI: 10.2196/26909] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 03/09/2021] [Accepted: 03/17/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Predictive alerts for impending hypoglycemic events enable persons with type 1 diabetes to take preventive actions and avoid serious consequences. OBJECTIVE This study aimed to develop a prediction model for hypoglycemic events with a low false alert rate, high sensitivity and specificity, and good generalizability to new patients and time periods. METHODS Performance improvement by focusing on sustained hypoglycemic events, defined as glucose values less than 70 mg/dL for at least 15 minutes, was explored. Two different modeling approaches were considered: (1) a classification-based method to directly predict sustained hypoglycemic events, and (2) a regression-based prediction of glucose at multiple time points in the prediction horizon and subsequent inference of sustained hypoglycemia. To address the generalizability and robustness of the model, two different validation mechanisms were considered: (1) patient-based validation (model performance was evaluated on new patients), and (2) time-based validation (model performance was evaluated on new time periods). RESULTS This study utilized data from 110 patients over 30-90 days comprising 1.6 million continuous glucose monitoring values under normal living conditions. The model accurately predicted sustained events with >97% sensitivity and specificity for both 30- and 60-minute prediction horizons. The false alert rate was kept to <25%. The results were consistent across patient- and time-based validation strategies. CONCLUSIONS Providing alerts focused on sustained events instead of all hypoglycemic events reduces the false alert rate and improves sensitivity and specificity. It also results in models that have better generalizability to new patients and time periods.
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Affiliation(s)
- Darpit Dave
- Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States
| | - Madhav Erraguntla
- Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States
| | - Mark Lawley
- Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States
| | - Daniel DeSalvo
- Department of Pediatrics, Baylor College of Medicine / Texas Children's Hospital, Houston, TX, United States
| | - Balakrishna Haridas
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
| | - Siripoom McKay
- Department of Pediatrics, Baylor College of Medicine / Texas Children's Hospital, Houston, TX, United States
| | - Chester Koh
- Division of Pediatric Urology, Texas Children's Hospital, Houston, TX, United States
- Scott Department of Urology, Baylor College of Medicine, Houston, TX, United States
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Personalized Advanced Time Blood Glucose Level Prediction. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-020-05263-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Saiti K, Macaš M, Lhotská L, Štechová K, Pithová P. Ensemble methods in combination with compartment models for blood glucose level prediction in type 1 diabetes mellitus. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 196:105628. [PMID: 32640369 DOI: 10.1016/j.cmpb.2020.105628] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Accepted: 06/21/2020] [Indexed: 06/11/2023]
Abstract
Backgroung: Type 1 diabetes is a disease that adversely affects the daily life of a large percentage of people worldwide. Daily glucose levels regulation and useful advices provided to patients regarding their diet are essential for diabetes treatment. For this reason, the interest of the academic community has focused on developing innovative systems, such as decision support systems, based on glucose prediction algorithms. The present work presents the predictive capabilities of ensemble methods compared to individual algorithms while combining each method with compartment models for fast acting insulin absorption simulation. Methods: An approach of combining widely used glycemia prediction algorithms is proposed and three different ensemble methods (Linear, Bagging and Boosting metaregressor) are applied and evaluated on their ability to provide accurate predictions for 30, 45 and 60 minutes ahead prediction horizon. Moreover, glycemia levels, long and short acting insulin dosages and consumed carbohydrates from six type one people with diabetes are used as input data and the results are evaluated in terms of root-mean square error and Clarke error grid analysis. Results: According to results, ensemble methods can provide more accurate glucose concentration in comparison to individual algorithms. Bagging metaregressor, specifically, performed better than individual algorithms in all prediction horizons for small datasets. Bagging ensemble method improved the percentage in zone A according to Clarkes error grid analysis by 4% and in some cases by 9%. Moreover, compartment models are proved to improve results in combination with any method at any prediction horizon. This strengthen the potential practical usefulness of the ensemble methods and the importance of building accurate compartment models.
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Affiliation(s)
- Kyriaki Saiti
- Department of Cybernetics, Czech Technical University in Prague, Czech Republic.
| | - Martin Macaš
- Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Czech Republic
| | - Lenka Lhotská
- Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Czech Republic
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Alfian G, Syafrudin M, Anshari M, Benes F, Atmaji FTD, Fahrurrozi I, Hidayatullah AF, Rhee J. Blood glucose prediction model for type 1 diabetes based on artificial neural network with time-domain features. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.10.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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15
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Fusion of Multiple Gridded Biomass Datasets for Generating a Global Forest Aboveground Biomass Map. REMOTE SENSING 2020. [DOI: 10.3390/rs12162559] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Many advanced satellite estimation methods have been developed, but global forest aboveground biomass (AGB) products remain largely uncertain. In this study, we explored data fusion techniques to generate a global forest AGB map for the 2000s at 0.01-degree resolution with improved accuracy by integrating ten existing local or global maps. The error removal and simple averaging algorithm, which is efficient and makes no assumption about the data and associated errors, was proposed to integrate these ten forest AGB maps. We first compiled the global reference AGB from in situ measurements and high-resolution AGB data that were originally derived from field data and airborne lidar data and determined the errors of each forest AGB map at the pixels with corresponding reference AGB values. Based on the errors determined from reference AGB data, the pixel-by-pixel errors associated with each of the ten AGB datasets were estimated from multiple predictors (e.g., leaf area index, forest canopy height, forest cover, land surface elevation, slope, temperature, and precipitation) using the random forest algorithm. The estimated pixel-by-pixel errors were then removed from the corresponding forest AGB datasets, and finally, global forest AGB maps were generated by combining the calibrated existing forest AGB datasets using the simple averaging algorithm. Cross-validation using reference AGB data showed that the accuracy of the fused global forest AGB map had an R-squared of 0.61 and a root mean square error (RMSE) of 53.68 Mg/ha, which is better than the reported accuracies (R-squared of 0.56 and RMSE larger than 80 Mg/ha) in the literature. Intercomparison with previous studies also suggested that the fused AGB estimates were much closer to the reference AGB values. This study attempted to integrate existing forest AGB datasets for generating a global forest AGB map with better accuracy and moved one step forward for our understanding of the global terrestrial carbon cycle by providing improved benchmarks of global forest carbon stocks.
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16
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Hameed H, Kleinberg S. Comparing Machine Learning Techniques for Blood Glucose Forecasting Using Free-living and Patient Generated Data. PROCEEDINGS OF MACHINE LEARNING RESEARCH 2020; 126:871-894. [PMID: 35072085 PMCID: PMC8782424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Managing a chronic disease like Type 1 diabetes (T1D) is both challenging and time consuming, but new technologies that allow continuous measurement of glucose and delivery of insulin have led to significant improvements. The development of an artificial pancreas (AP), which algorithmically determines insulin dosing and delivers insulin in a fully automated way, may transform T1D care but it is not yet widely available. Patient-led alternatives, like the Open Artificial Pancreas (OpenAPS), are being used by hundreds of individuals and have also led to a dramatic increase in the availability of patient generated health data (PGHD). All APs require an accurate forecast of blood glucose (BG). While there have been efforts to develop better forecasts and apply new ML techniques like deep learning to this problem, methods are often tested on small controlled datasets that do not indicate how they may perform in reality - and the most advanced methods have not always outperformed the simplest. We introduce a rigorous comparison of BG forecasting using both a small controlled research dataset and large heterogeneous PGHD. Our comparison advances the state of the art in BG forecasting by providing insight into how methods may fare when moving beyond small controlled studies to real-world use.
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Affiliation(s)
- Hadia Hameed
- Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA
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17
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Prediction of blood glucose concentration for type 1 diabetes based on echo state networks embedded with incremental learning. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.10.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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18
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The Role of Glycemic Index and Glycemic Load in the Development of Real-Time Postprandial Glycemic Response Prediction Models for Patients With Gestational Diabetes. Nutrients 2020; 12:nu12020302. [PMID: 31979294 PMCID: PMC7071209 DOI: 10.3390/nu12020302] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 01/18/2020] [Accepted: 01/20/2020] [Indexed: 12/21/2022] Open
Abstract
The incorporation of glycemic index (GI) and glycemic load (GL) is a promising way to improve the accuracy of postprandial glycemic response (PPGR) prediction for personalized treatment of gestational diabetes (GDM). Our aim was to assess the prediction accuracy for PPGR prediction models with and without GI data in women with GDM and healthy pregnant women. The GI values were sourced from University of Sydney’s database and assigned to a food database used in the mobile app DiaCompanion. Weekly continuous glucose monitoring (CGM) data for 124 pregnant women (90 GDM and 34 control) were analyzed together with records of 1489 food intakes. Pearson correlation (R) was used to quantify the accuracy of predicted PPGRs from the model relative to those obtained from CGM. The final model for incremental area under glucose curve (iAUC120) prediction chosen by stepwise multiple linear regression had an R of 0.705 when GI/GL was included among input variables and an R of 0.700 when GI/GL was not included. In linear regression with coefficients acquired using regularization methods, which was tested on the data of new patients, R was 0.584 for both models (with and without inclusion of GI/GL). In conclusion, the incorporation of GI and GL only slightly improved the accuracy of PPGR prediction models when used in remote monitoring.
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19
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Woldaregay AZ, Årsand E, Walderhaug S, Albers D, Mamykina L, Botsis T, Hartvigsen G. Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes. Artif Intell Med 2019; 98:109-134. [DOI: 10.1016/j.artmed.2019.07.007] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 08/22/2018] [Accepted: 07/19/2019] [Indexed: 10/26/2022]
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20
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De Falco I, Cioppa AD, Giugliano A, Marcelli A, Koutny T, Krcma M, Scafuri U, Tarantino E. A genetic programming-based regression for extrapolating a blood glucose-dynamics model from interstitial glucose measurements and their first derivatives. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.01.020] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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21
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Short-term prediction of glucose in type 1 diabetes using kernel adaptive filters. Med Biol Eng Comput 2018; 57:27-46. [PMID: 29967934 DOI: 10.1007/s11517-018-1859-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 06/11/2018] [Indexed: 10/28/2022]
Abstract
This study aims at presenting a nonlinear, recursive, multivariate prediction model of the subcutaneous glucose concentration in type 1 diabetes. Nonlinear regression is performed in a reproducing kernel Hilbert space, by either the fixed budget quantized kernel least mean square (QKLMS-FB) or the approximate linear dependency kernel recursive least-squares (KRLS-ALD) algorithm, such that a sparse model structure is accomplished. A multivariate feature set (i.e., subcutaneous glucose, food carbohydrates, insulin regime and physical activity) is used and its influence on short-term glucose prediction is investigated. The method is evaluated using data from 15 patients with type 1 diabetes in free-living conditions. In the case when all the input variables are considered: (i) the average root mean squared error (RMSE) of QKLMS-FB increases from 13.1 mg dL-1 (mean absolute percentage error (MAPE) 6.6%) for a 15-min prediction horizon (PH) to 37.7 mg dL-1 (MAPE 20.8%) for a 60-min PH and (ii) the RMSE of KRLS-ALD, being predictably lower, increases from 10.5 mg dL-1 (MAPE 5.2%) for a 15-min PH to 31.8 mg dL-1 (MAPE 18.0%) for a 60-min PH. Multivariate data improve systematically both the regularity and the time lag of the predictions, reducing the errors in critical glucose value regions for a PH ≥ 30 min. Graphical abstract ᅟ.
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22
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Contreras I, Vehi J. Artificial Intelligence for Diabetes Management and Decision Support: Literature Review. J Med Internet Res 2018; 20:e10775. [PMID: 29848472 PMCID: PMC6000484 DOI: 10.2196/10775] [Citation(s) in RCA: 175] [Impact Index Per Article: 29.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Revised: 05/15/2018] [Accepted: 05/15/2018] [Indexed: 01/03/2023] Open
Abstract
Background Artificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis. Objective The objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges. Methods A review of the literature was conducted using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 1849 pertinent articles, of which we selected 141 for detailed review. Results We propose a functional taxonomy for diabetes management and artificial intelligence. Additionally, a detailed analysis of each subject category was performed using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results. Conclusions We obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients’ quality of life.
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Affiliation(s)
- Ivan Contreras
- Modeling, Identification and Control Laboratory, Institut d'Informatica i Aplicacions, Universitat de Girona, Girona, Spain
| | - Josep Vehi
- Modeling, Identification and Control Laboratory, Institut d'Informatica i Aplicacions, Universitat de Girona, Girona, Spain.,Centro de Investigación Biomédica en Red de Diabetes y Enfermadades Metabólicas Asociadas, Girona, Spain
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23
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Yang J, Li L, Shi Y, Xie X. An ARIMA Model With Adaptive Orders for Predicting Blood Glucose Concentrations and Hypoglycemia. IEEE J Biomed Health Inform 2018; 23:1251-1260. [PMID: 29993728 DOI: 10.1109/jbhi.2018.2840690] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The continuous glucose monitoring system is an effective tool, which enables the users to monitor their blood glucose (BG) levels. Based on the continuous glucose monitoring (CGM) data, we aim at predicting future BG levels so that appropriate actions can be taken in advance to prevent hyperglycemia or hypoglycemia. Due to the time-varying nonstationarity of CGM data, verified by Augmented Dickey-Fuller test and analysis of variance, an autoregressive integrated moving average (ARIMA) model with an adaptive identification algorithm of model orders is proposed in the prediction framework. Such identification algorithm adaptively determines the model orders and simultaneously estimates the corresponding parameters using Akaike Information Criterion and least square estimation. A case study is conducted with the CGM data of diabetics under daily living conditions to analyze the prediction performance of the proposed model together with the early hypoglycemic alarms. Results show that the proposed model outperforms the adaptive univariate model and ARIMA model.
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24
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Yu X, Turksoy K, Rashid M, Feng J, Frantz N, Hajizadeh I, Samadi S, Sevil M, Lazaro C, Maloney Z, Littlejohn E, Quinn L, Cinar A. Model-Fusion-Based Online Glucose Concentration Predictions in People with Type 1 Diabetes. CONTROL ENGINEERING PRACTICE 2018; 71:129-141. [PMID: 29276347 PMCID: PMC5736323 DOI: 10.1016/j.conengprac.2017.10.013] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Accurate predictions of glucose concentrations are necessary to develop an artificial pancreas (AP) system for people with type 1 diabetes (T1D). In this work, a novel glucose forecasting paradigm based on a model fusion strategy is developed to accurately characterize the variability and transient dynamics of glycemic measurements. To this end, four different adaptive filters and a fusion mechanism are proposed for use in the online prediction of future glucose trajectories. The filter fusion mechanism is developed based on various prediction performance indexes to guide the overall output of the forecasting paradigm. The efficiency of the proposed model fusion based forecasting method is evaluated using simulated and clinical datasets, and the results demonstrate the capability and prediction accuracy of the data-based fusion filters, especially in the case of limited data availability. The model fusion framework may be used in the development of an AP system for glucose regulation in patients with T1D.
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Affiliation(s)
- Xia Yu
- School of Information Science and Engineering, Northeastern University, Shenyang 110819, PR China
| | - Kamuran Turksoy
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Mudassir Rashid
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Jianyuan Feng
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Nicole Frantz
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Iman Hajizadeh
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Sediqeh Samadi
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Mert Sevil
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Caterina Lazaro
- Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Zacharie Maloney
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Elizabeth Littlejohn
- Department of Pediatrics and Medicine, Kovler Diabetes Center, University of Chicago, Chicago, IL 60637, USA
| | - Laurie Quinn
- Department of Biobehavioral Health Science, College of Nursing, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Ali Cinar
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
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25
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Pustozerov E, Popova P, Tkachuk A, Bolotko Y, Yuldashev Z, Grineva E. Development and Evaluation of a Mobile Personalized Blood Glucose Prediction System for Patients With Gestational Diabetes Mellitus. JMIR Mhealth Uhealth 2018; 6:e6. [PMID: 29317385 PMCID: PMC5780619 DOI: 10.2196/mhealth.9236] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Revised: 12/08/2017] [Accepted: 12/08/2017] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Personalized blood glucose (BG) prediction for diabetes patients is an important goal that is pursued by many researchers worldwide. Despite many proposals, only a few projects are dedicated to the development of complete recommender system infrastructures that incorporate BG prediction algorithms for diabetes patients. The development and implementation of such a system aided by mobile technology is of particular interest to patients with gestational diabetes mellitus (GDM), especially considering the significant importance of quickly achieving adequate BG control for these patients in a short period (ie, during pregnancy) and a typically higher acceptance rate for mobile health (mHealth) solutions for short- to midterm usage. OBJECTIVE This study was conducted with the objective of developing infrastructure comprising data processing algorithms, BG prediction models, and an appropriate mobile app for patients' electronic record management to guide BG prediction-based personalized recommendations for patients with GDM. METHODS A mobile app for electronic diary management was developed along with data exchange and continuous BG signal processing software. Both components were coupled to obtain the necessary data for use in the personalized BG prediction system. Necessary data on meals, BG measurements, and other events were collected via the implemented mobile app and continuous glucose monitoring (CGM) system processing software. These data were used to tune and evaluate the BG prediction model, which included an algorithm for dynamic coefficients tuning. In the clinical study, 62 participants (GDM: n=49; control: n=13) took part in a 1-week monitoring trial during which they used the mobile app to track their meals and self-measurements of BG and CGM system for continuous BG monitoring. The data on 909 food intakes and corresponding postprandial BG curves as well as the set of patients' characteristics (eg, glycated hemoglobin, body mass index [BMI], age, and lifestyle parameters) were selected as inputs for the BG prediction models. RESULTS The prediction results by the models for BG levels 1 hour after food intake were root mean square error=0.87 mmol/L, mean absolute error=0.69 mmol/L, and mean absolute percentage error=12.8%, which correspond to an adequate prediction accuracy for BG control decisions. CONCLUSIONS The mobile app for the collection and processing of relevant data, appropriate software for CGM system signals processing, and BG prediction models were developed for a recommender system. The developed system may help improve BG control in patients with GDM; this will be the subject of evaluation in a subsequent study.
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Affiliation(s)
- Evgenii Pustozerov
- Department of Biomedical Engineering, Saint Petersburg State Electrotechnical University, Saint Petersburg, Russian Federation.,Institute of Endocrinology, Almazov National Medical Research Centre, Saint Petersburg, Russian Federation
| | - Polina Popova
- Institute of Endocrinology, Almazov National Medical Research Centre, Saint Petersburg, Russian Federation.,Department of Internal Diseases and Endocrinology, Pavlov First Saint Petersburg State Medical University, Saint Petersburg, Russian Federation
| | - Aleksandra Tkachuk
- Institute of Endocrinology, Almazov National Medical Research Centre, Saint Petersburg, Russian Federation
| | - Yana Bolotko
- Institute of Endocrinology, Almazov National Medical Research Centre, Saint Petersburg, Russian Federation
| | - Zafar Yuldashev
- Department of Biomedical Engineering, Saint Petersburg State Electrotechnical University, Saint Petersburg, Russian Federation
| | - Elena Grineva
- Institute of Endocrinology, Almazov National Medical Research Centre, Saint Petersburg, Russian Federation.,Department of Internal Diseases and Endocrinology, Pavlov First Saint Petersburg State Medical University, Saint Petersburg, Russian Federation
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26
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Contreras I, Oviedo S, Vettoretti M, Visentin R, Vehí J. Personalized blood glucose prediction: A hybrid approach using grammatical evolution and physiological models. PLoS One 2017; 12:e0187754. [PMID: 29112978 PMCID: PMC5675457 DOI: 10.1371/journal.pone.0187754] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Accepted: 10/25/2017] [Indexed: 11/19/2022] Open
Abstract
The large patient variability in human physiology and the effects of variables such as exercise or meals challenge current prediction modeling techniques. Physiological models are very precise but they are typically complex and specific physiological knowledge is required. In contrast, data-based models allow the incorporation of additional inputs and accurately capture the relationship between these inputs and the outcome, but at the cost of losing the physiological meaning of the model. In this work, we designed a hybrid approach comprising physiological models for insulin and grammatical evolution, taking into account the clinical harm caused by deviations from the target blood glucose by using a penalizing fitness function based on the Clarke error grid. The prediction models were built using data obtained over 14 days for 100 virtual patients generated by the UVA/Padova T1D simulator. Midterm blood glucose was predicted for the 100 virtual patients using personalized models and different scenarios. The results obtained were promising; an average of 98.31% of the predictions fell in zones A and B of the Clarke error grid. Midterm predictions using personalized models are feasible when the configuration of grammatical evolution explored in this study is used. The study of new alternative models is important to move forward in the development of alarm-and-control applications for the management of type 1 diabetes and the customization of the patient's treatments. The hybrid approach can be adapted to predict short-term blood glucose values to detect continuous glucose-monitoring sensor errors and to estimate blood glucose values when the continuous glucose-monitoring system fails to provide them.
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Affiliation(s)
- Iván Contreras
- Institut d’Informàtica i Aplicacions, Parc Científic i Tecnològic de la Universitat de Girona, Girona, Spain
- * E-mail:
| | - Silvia Oviedo
- Institut d’Informàtica i Aplicacions, Parc Científic i Tecnològic de la Universitat de Girona, Girona, Spain
| | - Martina Vettoretti
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Roberto Visentin
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Josep Vehí
- Institut d’Informàtica i Aplicacions, Parc Científic i Tecnològic de la Universitat de Girona, Girona, Spain
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Valentine WJ, Pollock RF, Saunders R, Bae J, Norrbacka K, Boye K. The Prime Diabetes Model: Novel Methods for Estimating Long-Term Clinical and Cost Outcomes in Type 1 Diabetes Mellitus. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2017; 20:985-991. [PMID: 28712629 DOI: 10.1016/j.jval.2016.12.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2016] [Accepted: 12/01/2016] [Indexed: 06/07/2023]
Abstract
BACKGROUND Recent publications describing long-term follow-up from landmark trials and diabetes registries represent an opportunity to revisit modeling options in type 1 diabetes mellitus (T1DM). OBJECTIVES To develop a new product-independent model capable of predicting long-term clinical and cost outcomes. METHODS After a systematic literature review to identify clinical trial and registry data, a model was developed (the PRIME Diabetes Model) to simulate T1DM progression and complication onset. The model runs as a patient-level simulation, making use of covariance matrices for cohort generation and risk factor progression, and simulating myocardial infarction, stroke, angina, heart failure, nephropathy, retinopathy, macular edema, neuropathy, amputation, hypoglycemia, ketoacidosis, mortality, and risk factor evolution. Several approaches novel to T1DM modeling were used, including patient characteristics and risk factor covariance, a glycated hemoglobin progression model derived from patient-level data, and model averaging approaches to evaluate complication risk. RESULTS Validation analyses comparing modeled outcomes with published studies demonstrated that the PRIME Diabetes Model projects long-term patient outcomes consistent with those reported for a number of long-term studies. Macrovascular end points were reliably reproduced across five different populations and microvascular complication risk was accurately predicted on the basis of comparisons with landmark studies and published registry data. CONCLUSIONS The PRIME Diabetes Model is product-independent, available online, and has been developed in line with good practice guidelines. Validation has indicated that outcomes from long-term studies can be reliably reproduced. The model offers new approaches to long-standing challenges in diabetes modeling and may become a valuable tool for informing health care policy.
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Affiliation(s)
| | | | - Rhodri Saunders
- Ossian Health Economics and Communications, Basel, Switzerland
| | - Jay Bae
- Eli Lilly and Company, Indianapolis, IN, USA
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28
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Oviedo S, Vehí J, Calm R, Armengol J. A review of personalized blood glucose prediction strategies for T1DM patients. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2017; 33:e2833. [PMID: 27644067 DOI: 10.1002/cnm.2833] [Citation(s) in RCA: 107] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Revised: 09/15/2016] [Accepted: 09/16/2016] [Indexed: 06/06/2023]
Abstract
This paper presents a methodological review of models for predicting blood glucose (BG) concentration, risks and BG events. The surveyed models are classified into three categories, and they are presented in summary tables containing the most relevant data regarding the experimental setup for fitting and testing each model as well as the input signals and the performance metrics. Each category exhibits trends that are presented and discussed. This document aims to be a compact guide to determine the modeling options that are currently being exploited for personalized BG prediction.
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Affiliation(s)
- Silvia Oviedo
- Institut d'Informàtica i Aplicacions, Parc Científic i Tecnològic de la Universitat de Girona, 17003, Girona, Spain
| | - Josep Vehí
- Institut d'Informàtica i Aplicacions, Universitat de Girona, Campus Montilivi, Edifici P4, 17071, Girona, Spain
| | - Remei Calm
- Institut d'Informàtica i Aplicacions, Universitat de Girona, Campus Montilivi, Edifici P4, 17071, Girona, Spain
| | - Joaquim Armengol
- Institut d'Informàtica i Aplicacions, Universitat de Girona, Campus Montilivi, Edifici P4, 17071, Girona, Spain
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29
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Towards Personalization of Diabetes Therapy Using Computerized Decision Support and Machine Learning: Some Open Problems and Challenges. SMART HEALTH 2015. [DOI: 10.1007/978-3-319-16226-3_10] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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