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Xu H, Yu H, Cheng Z, Mu C, Bao D, Li X, Xing Q. Development and validation of a prediction model for self-reported hypoglycemia risk in patients with type 2 diabetes: A longitudinal cohort study. J Diabetes Investig 2024; 15:468-482. [PMID: 38243656 PMCID: PMC10981142 DOI: 10.1111/jdi.14135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 10/21/2023] [Accepted: 12/09/2023] [Indexed: 01/21/2024] Open
Abstract
AIMS/INTRODUCTION To develop and validate a simple prediction model for hypoglycemia risk in patients with type 2 diabetes. MATERIALS AND METHODS We prospectively analyzed the data of 1,303 subjects in a third-class hospital in Tianjin and followed up their hypoglycemia events at 3 and 6 months. The hypoglycemia risk prediction models for 3 and 6 months were developed and the model performance was evaluated. RESULTS A total of 340 (28.4%) patients experienced hypoglycemia within 3 months and 462 (37.2%) within 6 months during the follow-up period. Age, central obesity, intensive insulin therapy, frequency of hypoglycemia in the past year, and hypoglycemia prevention education entered both model3month and model6month. The area under the receiver operating characteristic curve of model3month and model6month were 0.711 and 0.723, respectively. The Youden index was 0.315 and 0.361, while the sensitivities were 0.615 and 0.714, and the specificities were 0.717 and 0.631. The calibration curves showed that the models were similar to reality. The decision curves implied that the clinical net benefit of the model was clear. CONCLUSIONS The study developed 3 and 6 month hypoglycemia risk prediction models for patients with type 2 diabetes. The discrimination and calibration of the two prediction models were good, and might help to improve clinical decision-making and guide patients to more reasonable self-care and hypoglycemia prevention at home.
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Affiliation(s)
- Hongmei Xu
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien‐I Memorial Hospital & Tianjin Institute of EndocrinologyTianjin Medical UniversityTianjinChina
| | - Hangqing Yu
- Department of Respiratory and Critical CareThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Zhengnan Cheng
- Department of NursingTianjin Medical CollegeTianjinChina
| | - Chun Mu
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien‐I Memorial Hospital & Tianjin Institute of EndocrinologyTianjin Medical UniversityTianjinChina
| | - Di Bao
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien‐I Memorial Hospital & Tianjin Institute of EndocrinologyTianjin Medical UniversityTianjinChina
| | - Xiaohui Li
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien‐I Memorial Hospital & Tianjin Institute of EndocrinologyTianjin Medical UniversityTianjinChina
| | - Qiuling Xing
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien‐I Memorial Hospital & Tianjin Institute of EndocrinologyTianjin Medical UniversityTianjinChina
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Dave D, Vyas K, Branan K, McKay S, DeSalvo DJ, Gutierrez-Osuna R, Cote GL, Erraguntla M. Detection of Hypoglycemia and Hyperglycemia Using Noninvasive Wearable Sensors: Electrocardiograms and Accelerometry. J Diabetes Sci Technol 2024; 18:351-362. [PMID: 35927975 PMCID: PMC10973850 DOI: 10.1177/19322968221116393] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Monitoring glucose excursions is important in diabetes management. This can be achieved using continuous glucose monitors (CGMs). However, CGMs are expensive and invasive. Thus, alternative low-cost noninvasive wearable sensors capable of predicting glycemic excursions could be a game changer to manage diabetes. METHODS In this article, we explore two noninvasive sensor modalities, electrocardiograms (ECGs) and accelerometers, collected on five healthy participants over two weeks, to predict both hypoglycemic and hyperglycemic excursions. We extract 29 features encompassing heart rate variability features from the ECG, and time- and frequency-domain features from the accelerometer. We evaluated two machine learning approaches to predict glycemic excursions: a classification model and a regression model. RESULTS The best model for both hypoglycemia and hyperglycemia detection was the regression model based on ECG and accelerometer data, yielding 76% sensitivity and specificity for hypoglycemia and 79% sensitivity and specificity for hyperglycemia. This had an improvement of 5% in sensitivity and specificity for both hypoglycemia and hyperglycemia when compared with using ECG data alone. CONCLUSIONS Electrocardiogram is a promising alternative not only to detect hypoglycemia but also to predict hyperglycemia. Supplementing ECG data with contextual information from accelerometer data can improve glucose prediction.
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Affiliation(s)
- Darpit Dave
- Wm Michael Barnes '64 Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA
| | - Kathan Vyas
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA
| | - Kimberly Branan
- 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 Clinical Care Center, Houston, TX, USA
| | - Daniel J. DeSalvo
- Baylor College of Medicine, Houston, TX, USA
- Texas Children’s Hospital Clinical Care Center, Houston, TX, USA
| | - Ricardo Gutierrez-Osuna
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA
| | - Gerard L. Cote
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA
| | - Madhav Erraguntla
- Wm Michael Barnes '64 Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA
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Duckworth C, Guy MJ, Kumaran A, O’Kane AA, Ayobi A, Chapman A, Marshall P, Boniface M. Explainable Machine Learning for Real-Time Hypoglycemia and Hyperglycemia Prediction and Personalized Control Recommendations. J Diabetes Sci Technol 2024; 18:113-123. [PMID: 35695284 PMCID: PMC10899844 DOI: 10.1177/19322968221103561] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The occurrences of acute complications arising from hypoglycemia and hyperglycemia peak as young adults with type 1 diabetes (T1D) take control of their own care. Continuous glucose monitoring (CGM) devices provide real-time glucose readings enabling users to manage their control proactively. Machine learning algorithms can use CGM data to make ahead-of-time risk predictions and provide insight into an individual's longer term control. METHODS We introduce explainable machine learning to make predictions of hypoglycemia (<70 mg/dL) and hyperglycemia (>270 mg/dL) up to 60 minutes ahead of time. We train our models using CGM data from 153 people living with T1D in the CITY (CGM Intervention in Teens and Young Adults With Type 1 Diabetes)survey totaling more than 28 000 days of usage, which we summarize into (short-term, medium-term, and long-term) glucose control features along with demographic information. We use machine learning explanations (SHAP [SHapley Additive exPlanations]) to identify which features have been most important in predicting risk per user. RESULTS Machine learning models (XGBoost) show excellent performance at predicting hypoglycemia (area under the receiver operating curve [AUROC]: 0.998, average precision: 0.953) and hyperglycemia (AUROC: 0.989, average precision: 0.931) in comparison with a baseline heuristic and logistic regression model. CONCLUSIONS Maximizing model performance for glucose risk prediction and management is crucial to reduce the burden of alarm fatigue on CGM users. Machine learning enables more precise and timely predictions in comparison with baseline models. SHAP helps identify what about a CGM user's glucose control has led to predictions of risk which can be used to reduce their long-term risk of complications.
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Affiliation(s)
- Christopher Duckworth
- Electronics and Computer Science, IT Innovation Centre, University of Southampton, Southampton, UK
| | - Matthew J. Guy
- Department of Medical Physics, University Hospital Southampton NHS Foundation Trust, Southampton, UK
- Human-Computer Interaction for Health, University of Bristol, Bristol, UK
| | - Anitha Kumaran
- Child Health, Department of Endocrinology, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Aisling Ann O’Kane
- Human-Computer Interaction for Health, University of Bristol, Bristol, UK
- UCL Interaction Centre, University College London, London, UK
| | - Amid Ayobi
- Human-Computer Interaction for Health, University of Bristol, Bristol, UK
| | - Adriane Chapman
- Electronics and Computer Science, University of Southampton, Southampton, UK
| | - Paul Marshall
- Human-Computer Interaction for Health, University of Bristol, Bristol, UK
- UCL Interaction Centre, University College London, London, UK
| | - Michael Boniface
- Electronics and Computer Science, IT Innovation Centre, University of Southampton, Southampton, UK
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Thomsen HB, Jakobsen MM, Hecht-Pedersen N, Jensen MH, Kronborg T. Prediction of Hypoglycemia From Continuous Glucose Monitoring in Insulin-Treated Patients With Type 2 Diabetes Using Transfer Learning on Type 1 Diabetes Data: A Deep Transfer Learning Approach. J Diabetes Sci Technol 2023:19322968231215324. [PMID: 38014538 DOI: 10.1177/19322968231215324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
BACKGROUND Hypoglycemia is common in insulin-treated type 2 diabetes (T2D) patients, which can lead to decreased quality of life or premature death. Deep learning models offer promise of accurate predictions, but data scarcity poses a challenge. This study aims to develop a deep learning model utilizing transfer learning to predict hypoglycemia. METHODS Continuous glucose monitoring (CGM) data from 226 patients with type 1 diabetes (T1D) and 180 patients with T2D were utilized. Data were structured into one-hour samples and labeled as hypoglycemia or not depending on whether three consecutive CGM values were below 3.9 [mmol/L] (70 mg/dL) one hour after the sample. A convolutional neural network (CNN) was pre-trained with the T1D data set and subsequently fitted using a T2D data set, all while being optimized toward maximizing the area under the receiver operating characteristics curve (AUC) value, and it was externally validated on a separate T2D data set. RESULTS The developed model was externally validated with 334 711 one-hour CGM samples, of which 15 695 (4.69%) were labeled as hypoglycemic. The model achieved an AUC of 0.941 and a positive predictive value of 40.49% at a specificity of 95% and a sensitivity of 69.16%. CONCLUSIONS The transfer learned CNN model showed promising performance in predicting hypoglycemic episodes and with slightly better results than a non-transfer learned CNN model.
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Affiliation(s)
- Helene B Thomsen
- Department of Health Science and Technology, Aalborg University, Gistrup, Denmark
| | - Mike M Jakobsen
- Department of Health Science and Technology, Aalborg University, Gistrup, Denmark
| | | | - Morten Hasselstrøm Jensen
- Department of Health Science and Technology, Aalborg University, Gistrup, Denmark
- Data Science, Novo Nordisk A/S, Søborg, Denmark
| | - Thomas Kronborg
- Department of Health Science and Technology, Aalborg University, Gistrup, Denmark
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5
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Robinson R, Liday C, Lee S, Williams IC, Wright M, An S, Nguyen E. Artificial Intelligence in Health Care-Understanding Patient Information Needs and Designing Comprehensible Transparency: Qualitative Study. JMIR AI 2023; 2:e46487. [PMID: 38333424 PMCID: PMC10851077 DOI: 10.2196/46487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
Background Artificial intelligence (AI) is as a branch of computer science that uses advanced computational methods such as machine learning (ML), to calculate and/or predict health outcomes and address patient and provider health needs. While these technologies show great promise for improving healthcare, especially in diabetes management, there are usability and safety concerns for both patients and providers about the use of AI/ML in healthcare management. Objectives To support and ensure safe use of AI/ML technologies in healthcare, the team worked to better understand: 1) patient information and training needs, 2) the factors that influence patients' perceived value and trust in AI/ML healthcare applications; and 3) on how best to support safe and appropriate use of AI/ML enabled devices and applications among people living with diabetes. Methods To understand general patient perspectives and information needs related to the use of AI/ML in healthcare, we conducted a series of focus groups (n=9) and interviews (n=3) with patients (n=40) and interviews with providers (n=6) in Alaska, Idaho, and Virginia. Grounded Theory guided data gathering, synthesis, and analysis. Thematic content and constant comparison analysis were used to identify relevant themes and sub-themes. Inductive approaches were used to link data to key concepts including preferred patient-provider-interactions, patient perceptions of trust, accuracy, value, assurances, and information transparency. Results Key summary themes and recommendations focused on: 1) patient preferences for AI/ML enabled device and/or application information; 2) patient and provider AI/ML-related device and/or application training needs; 3) factors contributing to patient and provider trust in AI/ML enabled devices and/or application; and 4) AI/ML-related device and/or application functionality and safety considerations. A number of participant (patients and providers) recommendations to improve device functionality to guide information and labeling mandates (e.g., links to online video resources, and access to 24/7 live in-person or virtual emergency support). Other patient recommendations include: 1) access to practice devices; 2) connection to local supports and reputable community resources; 3) simplified display and alert limits. Conclusion Recommendations from both patients and providers could be used by Federal Oversight Agencies to improve utilization of AI/ML monitoring of technology use in diabetes, improving device safety and efficacy.
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Affiliation(s)
- Renee Robinson
- College of Pharmacy, Idaho State University, Anchorage, AK, US
| | - Cara Liday
- College of Pharmacy, Idaho State University, Pocatello, ID, US
| | - Sarah Lee
- College of Pharmacy, Idaho State University, Meridian, ID, US
| | - Ishan C Williams
- School of Nursing, University of Virginia, Charlottesville, VA, US
| | - Melanie Wright
- College of Pharmacy, Idaho State University, Meridian, ID, US
| | - Sungjoon An
- College of Pharmacy, Idaho State University, Meridian, ID, US
| | - Elaine Nguyen
- College of Pharmacy, Idaho State University, Meridian, ID, US
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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] [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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7
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Stredny C, Rotenberg A, Leviton A, Loddenkemper T. Systemic inflammation as a biomarker of seizure propensity and a target for treatment to reduce seizure propensity. Epilepsia Open 2023; 8:221-234. [PMID: 36524286 PMCID: PMC9978091 DOI: 10.1002/epi4.12684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022] Open
Abstract
People with diabetes can wear a device that measures blood glucose and delivers just the amount of insulin needed to return the glucose level to within bounds. Currently, people with epilepsy do not have access to an equivalent wearable device that measures a systemic indicator of an impending seizure and delivers a rapidly acting medication or other intervention (e.g., an electrical stimulus) to terminate or prevent a seizure. Given that seizure susceptibility is reliably increased in systemic inflammatory states, we propose a novel closed-loop device where release of a fast-acting therapy is governed by sensors that quantify the magnitude of systemic inflammation. Here, we review the evidence that patients with epilepsy have raised levels of systemic indicators of inflammation than controls, and that some anti-inflammatory drugs have reduced seizure occurrence in animals and humans. We then consider the options of what might be incorporated into a responsive anti-seizure system.
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Affiliation(s)
- Coral Stredny
- Division of Epilepsy and Clinical Neurophysiology, Department of NeurologyBoston Children's HospitalBostonMassachusettsUSA
- Department of NeurologyHarvard Medical SchoolBostonMassachusettsUSA
| | - Alexander Rotenberg
- Division of Epilepsy and Clinical Neurophysiology, Department of NeurologyBoston Children's HospitalBostonMassachusettsUSA
- Department of NeurologyHarvard Medical SchoolBostonMassachusettsUSA
| | - Alan Leviton
- Division of Epilepsy and Clinical Neurophysiology, Department of NeurologyBoston Children's HospitalBostonMassachusettsUSA
- Department of NeurologyHarvard Medical SchoolBostonMassachusettsUSA
| | - Tobias Loddenkemper
- Division of Epilepsy and Clinical Neurophysiology, Department of NeurologyBoston Children's HospitalBostonMassachusettsUSA
- Department of NeurologyHarvard Medical SchoolBostonMassachusettsUSA
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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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Fleischer J, Hansen TK, Cichosz SL. Hypoglycemia event prediction from CGM using ensemble learning. FRONTIERS IN CLINICAL DIABETES AND HEALTHCARE 2022; 3:1066744. [PMID: 36992787 PMCID: PMC10012121 DOI: 10.3389/fcdhc.2022.1066744] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 11/22/2022] [Indexed: 12/14/2022]
Abstract
This work sought to explore the potential of using standalone continuous glucose monitor (CGM) data for the prediction of hypoglycemia utilizing a large cohort of type 1 diabetes patients during free-living. We trained and tested an algorithm for the prediction of hypoglycemia within 40 minutes on 3.7 million CGM measurements from 225 patients using ensemble learning. The algorithm was also validated using 11.5 million synthetic CGM data. The results yielded a receiver operating characteristic area under the curve (ROC AUC) of 0.988 and a precision-recall area under the curve (PR AUC) of 0.767. In an event-based analysis for predicting hypoglycemic events, the algorithm had a sensitivity of 90%, a lead-time of 17.5 minutes and a false-positive rate of 38%. In conclusion, this work demonstrates the potential of using ensemble learning to predict hypoglycemia, using only CGM data. This could help alarm patients of a future hypoglycemic event so countermeasures can be initiated.
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Affiliation(s)
- Jesper Fleischer
- Steno Diabetes Center Aarhus, Aarhus, Denmark
- Steno Diabetes Center Zealand, Holbæk, Denmark
| | | | - Simon Lebech Cichosz
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
- *Correspondence: Simon Lebech Cichosz,
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Aljihmani L, Kerdjidj O, Petrovski G, Erraguntla M, Sasangohar F, Mehta RK, Qaraqe K. Hand tremor-based hypoglycemia detection and prediction in adolescents with type 1 diabetes. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103869] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Jahromi R, Zahed K, Sasangohar F, Erraguntla M, Mehta R, Qaraqe K. Hypoglycemia Detection Using Hand Tremors: A Home Study in Patients with Type 1 Diabetes (Preprint). JMIR Diabetes 2022; 8:e40990. [PMID: 37074783 PMCID: PMC10157461 DOI: 10.2196/40990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 01/26/2023] [Accepted: 02/20/2023] [Indexed: 02/22/2023] Open
Abstract
BACKGROUND Diabetes affects millions of people worldwide and is steadily increasing. A serious condition associated with diabetes is low glucose levels (hypoglycemia). Monitoring blood glucose is usually performed by invasive methods or intrusive devices, and these devices are currently not available to all patients with diabetes. Hand tremor is a significant symptom of hypoglycemia, as nerves and muscles are powered by blood sugar. However, to our knowledge, no validated tools or algorithms exist to monitor and detect hypoglycemic events via hand tremors. OBJECTIVE In this paper, we propose a noninvasive method to detect hypoglycemic events based on hand tremors using accelerometer data. METHODS We analyzed triaxial accelerometer data from a smart watch recorded from 33 patients with type 1 diabetes for 1 month. Time and frequency domain features were extracted from acceleration signals to explore different machine learning models to classify and differentiate between hypoglycemic and nonhypoglycemic states. RESULTS The mean duration of the hypoglycemic state was 27.31 (SD 5.15) minutes per day for each patient. On average, patients had 1.06 (SD 0.77) hypoglycemic events per day. The ensemble learning model based on random forest, support vector machines, and k-nearest neighbors had the best performance, with a precision of 81.5% and a recall of 78.6%. The results were validated using continuous glucose monitor readings as ground truth. CONCLUSIONS Our results indicate that the proposed approach can be a potential tool to detect hypoglycemia and can serve as a proactive, nonintrusive alert mechanism for hypoglycemic events.
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Affiliation(s)
- Reza Jahromi
- Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, United States
| | - Karim Zahed
- Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States
| | - Farzan Sasangohar
- Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States
- Center for Critical Care, Houston Methodist Hospital, Houston, TX, United States
| | - Madhav Erraguntla
- Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States
| | - Ranjana Mehta
- Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States
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Zale AD, Abusamaan MS, McGready J, Mathioudakis N. Development and validation of a machine learning model for classification of next glucose measurement in hospitalized patients. EClinicalMedicine 2022; 44:101290. [PMID: 35169690 PMCID: PMC8829081 DOI: 10.1016/j.eclinm.2022.101290] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 01/13/2022] [Accepted: 01/18/2022] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Inpatient glucose management can be challenging due to evolving factors that influence a patient's blood glucose (BG) throughout hospital admission. The purpose of our study was to predict the category of a patient's next BG measurement based on electronic medical record (EMR) data. METHODS EMR data from 184,361 admissions containing 4,538,418 BG measurements from five hospitals in the Johns Hopkins Health System were collected from patients who were discharged between January 1, 2015 and May 31, 2019. Index BGs used for prediction included the 5th to penultimate BG measurements (N = 2,740,539). The outcome was category of next BG measurement: hypoglycemic (BG ≤ 70 mg/dl), controlled (BG 71-180 mg/dl), or hyperglycemic (BG > 180 mg/dl). A random forest algorithm that included a broad range of clinical covariates predicted the outcome and was validated internally and externally. FINDINGS In our internal validation test set, 72·8%, 25·7%, and 1·5% of BG measurements occurring after the index BG were controlled, hyperglycemic, and hypoglycemic respectively. The sensitivity/specificity for prediction of controlled, hyperglycemic, and hypoglycemic were 0·77/0·81, 0·77/0·89, and 0·73/0·91, respectively. On external validation in four hospitals, the ranges of sensitivity/specificity for prediction of controlled, hyperglycemic, and hypoglycemic were 0·64-0·70/0·80-0·87, 0·75-0·80/0·82-0·84, and 0·76-0·78/0·87-0·90, respectively. INTERPRETATION A machine learning algorithm using EMR data can accurately predict the category of a hospitalized patient's next BG measurement. Further studies should determine the effectiveness of integration of this model into the EMR in reducing rates of hypoglycemia and hyperglycemia.
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Key Words
- AUC, area under receiver operating curve
- BG, blood glucose
- BMI, body mass index
- CGM, continuous glucose monitor
- EMR, electronic medical record
- ICD, International Classification of Diseases
- ICU, intensive care unit
- NLR, negative likelihood ratio
- NPO, nil per os
- NPV, negative predictive value
- PLR, positive likelihood ratio
- PPV, positive predictive value
- T1DM, type 1 diabetes mellitus
- T2DM, type 2 diabetes mellitus
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Affiliation(s)
- Andrew D. Zale
- Associate Professor of Medicine, Division of Endocrinology, Diabetes & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, 1830 E. Monument Street Suite 333, Baltimore, MD 21287, USA
| | - Mohammed S. Abusamaan
- Associate Professor of Medicine, Division of Endocrinology, Diabetes & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, 1830 E. Monument Street Suite 333, Baltimore, MD 21287, USA
| | - John McGready
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Nestoras Mathioudakis
- Associate Professor of Medicine, Division of Endocrinology, Diabetes & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, 1830 E. Monument Street Suite 333, Baltimore, MD 21287, USA
- Corresponding author.
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13
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Tsichlaki S, Koumakis L, Tsiknakis M. A Systematic Review of T1D Hypoglycemia Prediction Algorithms (Preprint). JMIR Diabetes 2021; 7:e34699. [PMID: 35862181 PMCID: PMC9353679 DOI: 10.2196/34699] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 04/02/2022] [Accepted: 04/20/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Stella Tsichlaki
- Department of Electrical & Computer Engineering, Hellenic Mediterranean University, Heraklion, Greece
| | - Lefteris Koumakis
- Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece
| | - Manolis Tsiknakis
- Department of Electrical & Computer Engineering, Hellenic Mediterranean University, Heraklion, Greece
- Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece
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Cichosz SL, Kronborg T, Jensen MH, Hejlesen O. Penalty weighted glucose prediction models could lead to better clinically usage. Comput Biol Med 2021; 138:104865. [PMID: 34543891 DOI: 10.1016/j.compbiomed.2021.104865] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 08/27/2021] [Accepted: 09/10/2021] [Indexed: 12/23/2022]
Abstract
BACKGROUND AND OBJECTIVE Numerous attempts to predict glucose value from continuous glucose monitors (CGM) have been published. However, there is a lack of proper analysis and modeling of penalty for errors in different glycemic ranges. The aim of this study was to investigate the potential for developing glucose prediction models with focus on the clinical aspects. METHODS We developed and compared six different models to test which approach were best suited for predicting glucose levels at different lead times between 10 and 60 min. The models were: last observation carried forward, linear extrapolation, ensemble methods using LSBoost and bagging, neural networks, one without error-weights and one with error-weights. The modeling and test were based on 225 type 1 diabetes patients with 315,000 h of CGM data. RESULTS Results show that it is possible to predict glucose levels based on CGM with a reasonable accuracy and precision with a 30-min prediction lead time. A comparison of different methods shows that there are improvements on performance gained from using more advanced machine learning algorithms (MARD 10.26-10.79 @ 30-min lead time) compared to a simple modeling (MARD 10.75-12.97 @ 30-min lead time). Moreover, the proposed use of error weights could lead to better clinical performance of these models, which is an important factor for real usage. E.g., the percentages in the C-zone of the consensus error grid without error-weights (0.57-0.68%) vs including error-weights (0.28%). CONCLUSIONS The results point toward that using error weighting in the training of the models could lead to better clinical performance.
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Affiliation(s)
| | - Thomas Kronborg
- Department of Health Science and Technology, Aalborg University, Denmark
| | - Morten Hasselstrøm Jensen
- Department of Health Science and Technology, Aalborg University, Denmark; Steno Diabetes Center North Denmark, Aalborg University Hospital, Aalborg, Denmark
| | - Ole Hejlesen
- Department of Health Science and Technology, Aalborg University, Denmark
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