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Chen X, Zhou S, Yang L, Zhong Q, Liu H, Zhang Y, Yu H, Cai Y. Risk Prediction of Diabetes Progression Using Big Data Mining with Multifarious Physical Examination Indicators. Diabetes Metab Syndr Obes 2024; 17:1249-1265. [PMID: 38496004 PMCID: PMC10942017 DOI: 10.2147/dmso.s449955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/25/2024] [Indexed: 03/19/2024] Open
Abstract
Purpose The purpose of this study is to explore the independent-influencing factors from normal people to prediabetes and from prediabetes to diabetes and use different prediction models to build diabetes prediction models. Methods The original data in this retrospective study are collected from the participants who took physical examinations in the Health Management Center of Peking University Shenzhen Hospital. Regression analysis is individually applied between the populations of normal and prediabetes, as well as the populations of prediabetes and diabetes, for feature selection. Afterward,the independent influencing factors mentioned above are used as predictive factors to construct a prediction model. Results Selecting physical examination indicators for training different ML models through univariate and multivariate logistic regression, the study finds Age, PRO, TP, and ALT are four independent risk factors for normal people to develop prediabetes, and GLB and HDL.C are two independent protective factors, while logistic regression performs best on the testing set (Acc: 0.76, F-measure: 0.74, AUC: 0.78). We also find Age, Gender, BMI, SBP, U.GLU, PRO, ALT, and TG are independent risk factors for prediabetes people to diabetes, and AST is an independent protective factor, while logistic regression performs best on the testing set (Acc: 0.86, F-measure: 0.84, AUC: 0.74). Conclusion The discussion of the clinical relationships between these indicators and diabetes supports the interpretability of our feature selection. Among four prediction models, the logistic regression model achieved the best performance on the testing set.
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Affiliation(s)
- Xiaohong Chen
- Center of Health Management, Peking University Shenzhen Hospital, Shenzhen, People’s Republic of China
| | - Shiqi Zhou
- School of Future Technology, South China University of Technology, Guangzhou, People’s Republic of China
| | - Lin Yang
- Center of Health Management, Peking University Shenzhen Hospital, Shenzhen, People’s Republic of China
| | - Qianqian Zhong
- Center of Health Management, Peking University Shenzhen Hospital, Shenzhen, People’s Republic of China
| | - Hongguang Liu
- Center of Health Management, Huazhong University of Science and Technology Union Hospital (Nanshan Hospital), Shenzhen, People’s Republic of China
| | - Yongjian Zhang
- Center of Health Management, Peking University Shenzhen Hospital, Shenzhen, People’s Republic of China
| | - Hanyi Yu
- School of Future Technology, South China University of Technology, Guangzhou, People’s Republic of China
| | - Yongjiang Cai
- Center of Health Management, Peking University Shenzhen Hospital, Shenzhen, People’s Republic of China
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Di Filippo D, Sunstrum FN, Khan JU, Welsh AW. Non-Invasive Glucose Sensing Technologies and Products: A Comprehensive Review for Researchers and Clinicians. SENSORS (BASEL, SWITZERLAND) 2023; 23:9130. [PMID: 38005523 PMCID: PMC10674292 DOI: 10.3390/s23229130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 11/01/2023] [Accepted: 11/08/2023] [Indexed: 11/26/2023]
Abstract
Diabetes Mellitus incidence and its negative outcomes have dramatically increased worldwide and are expected to further increase in the future due to a combination of environmental and social factors. Several methods of measuring glucose concentration in various body compartments have been described in the literature over the years. Continuous advances in technology open the road to novel measuring methods and innovative measurement sites. The aim of this comprehensive review is to report all the methods and products for non-invasive glucose measurement described in the literature over the past five years that have been tested on both human subjects/samples and tissue models. A literature review was performed in the MDPI database, with 243 articles reviewed and 124 included in a narrative summary. Different comparisons of techniques focused on the mechanism of action, measurement site, and machine learning application, outlining the main advantages and disadvantages described/expected so far. This review represents a comprehensive guide for clinicians and industrial designers to sum the most recent results in non-invasive glucose sensing techniques' research and production to aid the progress in this promising field.
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Affiliation(s)
- Daria Di Filippo
- Discipline of Women’s Health, School of Clinical Medicine, Faculty of Medicine, University of New South Wales, Sydney, NSW 2052, Australia;
| | - Frédérique N. Sunstrum
- Product Design, School of Design, Faculty of Design, Architecture and Built Environment, University of Technology Sydney, Sydney, NSW 2007, Australia;
| | - Jawairia U. Khan
- Institute for Biomedical Materials and Devices, School of Mathematical and Physical Sciences, Faculty of Science, University of Technology Sydney, Sydney, NSW 2007, Australia;
| | - Alec W. Welsh
- Discipline of Women’s Health, School of Clinical Medicine, Faculty of Medicine, University of New South Wales, Sydney, NSW 2052, Australia;
- Department of Maternal-Fetal Medicine, Royal Hospital for Women, Randwick, NSW 2031, Australia
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3
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Liu Y, Feng W, Lou J, Qiu W, Shen J, Zhu Z, Hua Y, Zhang M, Billong LF. Performance of a prediabetes risk prediction model: A systematic review. Heliyon 2023; 9:e15529. [PMID: 37215820 PMCID: PMC10196520 DOI: 10.1016/j.heliyon.2023.e15529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 04/11/2023] [Accepted: 04/12/2023] [Indexed: 05/24/2023] Open
Abstract
Backgrounds The prediabetes population is large and easily overlooked because of the lack of obvious symptoms, which can progress to diabetes. Early screening and targeted interventions can substantially reduce the rate of conversion of prediabetes to diabetes. Therefore, this study systematically reviewed prediabetes risk prediction models, performed a summary and quality evaluation, and aimed to recommend the optimal model. Methods We systematically searched five databases (Cochrane, PubMed, Embase, Web Of Science, and CNKI) for published literature related to prediabetes risk prediction models and excluded preprints, duplicate publications, reviews, editorials, and other studies, with a search time frame of March 01, 2023. Data were categorized and summarized using a standardized data extraction form that extracted data including author; publication date; study design; country; demographic characteristics; assessment tool name; sample size; study type; and model-related indicators. The PROBAST tool was used to assess the risk of bias profile of included studies. Findings 14 studies with a total of 15 models were eventually included in the systematic review. We found that the most common predictors of models were age, family history of diabetes, gender, history of hypertension, and BMI. Most of the studies (83.3%) had a high risk of bias, mainly related to under-reporting of outcome information and poor methodological design during the development and validation of models. Due to the low quality of included studies, the evidence for predictive validity of the available models is unclear. Interpretation We should pay attention to the early screening of prediabetes patients and give timely pharmacological and lifestyle interventions. The predictive performance of the existing model is not satisfactory, and the model building process can be standardized and external validation can be added to improve the accuracy of the model in the future.
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Affiliation(s)
- Yujin Liu
- Schools of Nursing and Medicine, Huzhou University, Huzhou, 313000, China
| | - Wenming Feng
- Huzhou First People's Hospital, Huzhou, 313000, China
| | - Jianlin Lou
- Huzhou Key Laboratory of Precise Prevention and Control of Major Chronic Diseases, Huzhou University, Huzhou, 313000, China
| | - Wei Qiu
- Department of Endocrinology, Huzhou Central Hospital, Huzhou, 313000, China
| | - Jiantong Shen
- Schools of Nursing and Medicine, Huzhou University, Huzhou, 313000, China
- Huzhou Key Laboratory of Precise Prevention and Control of Major Chronic Diseases, Huzhou University, Huzhou, 313000, China
| | - Zhichao Zhu
- Schools of Nursing and Medicine, Huzhou University, Huzhou, 313000, China
- Internal Medicine General Ward, Jinhua Municipal Central Hospital Medical Group, Jinhua, 321200, China
| | - Yuting Hua
- Schools of Nursing and Medicine, Huzhou University, Huzhou, 313000, China
| | - Mei Zhang
- Schools of Nursing and Medicine, Huzhou University, Huzhou, 313000, China
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Zou X, Liu Y, Ji L. Review: Machine learning in precision pharmacotherapy of type 2 diabetes-A promising future or a glimpse of hope? Digit Health 2023; 9:20552076231203879. [PMID: 37786401 PMCID: PMC10541760 DOI: 10.1177/20552076231203879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 09/08/2023] [Indexed: 10/04/2023] Open
Abstract
Precision pharmacotherapy of diabetes requires judicious selection of the optimal therapeutic agent for individual patients. Artificial intelligence (AI), a swiftly expanding discipline, holds substantial potential to transform current practices in diabetes diagnosis and management. This manuscript provides a comprehensive review of contemporary research investigating drug responses in patient subgroups, stratified via either supervised or unsupervised machine learning approaches. The prevalent algorithmic workflow for investigating drug responses using machine learning involves cohort selection, data processing, predictor selection, development and validation of machine learning methods, subgroup allocation, and subsequent analysis of drug response. Despite the promising feature, current research does not yet provide sufficient evidence to implement machine learning algorithms into routine clinical practice, due to a lack of simplicity, validation, or demonstrated efficacy. Nevertheless, we anticipate that the evolving evidence base will increasingly substantiate the role of machine learning in molding precision pharmacotherapy for diabetes.
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Affiliation(s)
- Xiantong Zou
- Xiantong Zou, Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, 100044, China.
| | | | - Linong Ji
- Linong Ji, Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, 100044, China.
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An Untargeted Lipidomics Study of Acute Ischemic Stroke with Hyperglycemia Based on Ultrahigh-Performance Liquid Chromatography-Mass Spectrometry. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8332278. [PMID: 36060656 PMCID: PMC9439902 DOI: 10.1155/2022/8332278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 08/01/2022] [Accepted: 08/04/2022] [Indexed: 11/21/2022]
Abstract
Patients with type 2 diabetes have twice as much of the risk of acute ischemic stroke (AIS) occurrence as healthy individuals, and the AIS patients with type 2 diabetes have a higher risk of death and a poorer prognosis. This study was to investigate the interrelationship between hyperglycemia and AIS and provided a reference for blood glucose management of AIS patients. The blood glucose level of AIS patients of the present study was controlled by insulin below 180 mg/dL (standard group) and between 80 and 130 mg/dL (management group). And the fasting venous blood samples were collected for determination of blood glucose level, homeostasis model assessment of insulin resistance (HOMA-IR), peptide C, and basal insulin level. Furthermore, lipids of the blood samples were detected using metabolomics, so as to clarify the similarities and differences in metabolic patterns in AIS patients with diabetes after the intervention of different glycemic strategies. The results revealed that compared to the standard group, the blood glucose level and HOMA-IR in the management group were significantly decreased, and levels of peptide C and basal insulin level were greatly increased. Through lipidomics detection, 83, 50, and 44 types of significantly upregulated differential lipids were detected in the standard vs. normal groups, the standard vs. management groups, and the management vs. normal groups, respectively, with triacylglycerol dominated. This study preliminarily revealed metabolic differences among AIS patients with hyperglycemia after different blood glucose intervention methods, hoping to provide a theoretical basis for clinical prevention and treatment of this disease.
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Classification of Electrocardiography Hybrid Convolutional Neural Network-Long Short Term Memory with Fully Connected Layer. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6348424. [PMID: 35860642 PMCID: PMC9293511 DOI: 10.1155/2022/6348424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 05/23/2022] [Indexed: 11/26/2022]
Abstract
Electrocardiography (ECG) is a technique for observing and recording the electrical activity of the human heart. The usage of an ECG signal is common among clinical professionals in the collection of time data for the examination of any rhythmic conditions associated with a subject. The investigation was carried out in order to computerize the assignment by exhibiting the issue using encoder-decoder techniques, creating the information that was simply typical of it, and utilising misfortune appropriation to anticipate standard or anomalous information. On a broad variety of applications such as voice recognition and prediction, the long short-term memory (LSTM) fully connected layer (FCL) and the two convolutional neural networks (CNNs) have shown superior performance over deep learning networks (DLNs). DNNs are suitable for making high points for a more divisible region and CNNs are suitable for reducing recurrence types, LSTMs are appropriate for temporary displays, in the same way as CNNs are appropriate for reducing recurrence types. The CNN, LSTM, and DNN algorithms are acceptable for viewing. The complementarity of DNNs, CNNs, and LSTMs was investigated in this research by bringing them all together under the single architectural company. The researchers got the ECG data from the MIT-BIH arrhythmia database as a result of the investigation. Our results demonstrate that the approach proposed may expressively describe ECG series and identify abnormalities via scores that outperform existing supervised and unsupervised methods in both the short term and long term. The LSTM network and FCL additionally demonstrated that the unbalanced datasets associated with the ECG beat detection problem could be consistently resolved and that they were not susceptible to the accuracy of ECG signals. It is recommended that cardiologists employ the unique technique to aid them in performing reliable and impartial interpretation of ECG data in telemedicine settings.
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Zanelli S, Ammi M, Hallab M, El Yacoubi MA. Diabetes Detection and Management through Photoplethysmographic and Electrocardiographic Signals Analysis: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:4890. [PMID: 35808386 PMCID: PMC9269150 DOI: 10.3390/s22134890] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/17/2022] [Accepted: 06/21/2022] [Indexed: 06/15/2023]
Abstract
(1) Background: Diabetes mellitus (DM) is a chronic, metabolic disease characterized by elevated levels of blood glucose. Recently, some studies approached the diabetes care domain through the analysis of the modifications of cardiovascular system parameters. In fact, cardiovascular diseases are the first leading cause of death in diabetic subjects. Thanks to their cost effectiveness and their ease of use, electrocardiographic (ECG) and photoplethysmographic (PPG) signals have recently been used in diabetes detection, blood glucose estimation and diabetes-related complication detection. This review's aim is to provide a detailed overview of all the published methods, from the traditional (non machine learning) to the deep learning approaches, to detect and manage diabetes using PPG and ECG signals. This review will allow researchers to compare and understand the differences, in terms of results, amount of data and complexity that each type of approach provides and requires. (2) Method: We performed a systematic review based on articles that focus on the use of ECG and PPG signals in diabetes care. The search was focused on keywords related to the topic, such as "Diabetes", "ECG", "PPG", "Machine Learning", etc. This was performed using databases, such as PubMed, Google Scholar, Semantic Scholar and IEEE Xplore. This review's aim is to provide a detailed overview of all the published methods, from the traditional (non machine learning) to the deep learning approaches, to detect and manage diabetes using PPG and ECG signals. This review will allow researchers to compare and understand the differences, in terms of results, amount of data and complexity that each type of approach provides and requires. (3) Results: A total of 78 studies were included. The majority of the selected studies focused on blood glucose estimation (41) and diabetes detection (31). Only 7 studies focused on diabetes complications detection. We present these studies by approach: traditional, machine learning and deep learning approaches. (4) Conclusions: ECG and PPG analysis in diabetes care showed to be very promising. Clinical validation and data processing standardization need to be improved in order to employ these techniques in a clinical environment.
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Affiliation(s)
- Serena Zanelli
- University of Paris 8, LAGA, CNRS, Institut Galilée, 93200 Saint Denis, France;
- SAMOVAR Telecom SudParis, CNRS, Institut Polytechnique de Paris, 91764 Paris, France;
| | - Mehdi Ammi
- University of Paris 8, LAGA, CNRS, Institut Galilée, 93200 Saint Denis, France;
| | | | - Mounim A. El Yacoubi
- SAMOVAR Telecom SudParis, CNRS, Institut Polytechnique de Paris, 91764 Paris, France;
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8
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Petmezas G, Stefanopoulos L, Kilintzis V, Tzavelis A, Rogers JA, Katsaggelos AK, Maglaveras N. State-of-the-art Deep Learning Methods on Electrocardiogram Data: A Systematic Review (Preprint). JMIR Med Inform 2022; 10:e38454. [PMID: 35969441 PMCID: PMC9425174 DOI: 10.2196/38454] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 06/03/2022] [Accepted: 07/03/2022] [Indexed: 11/13/2022] Open
Abstract
Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals. Objective This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications. Methods The PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches. Results We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models. Conclusions We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.
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Affiliation(s)
- Georgios Petmezas
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Leandros Stefanopoulos
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vassilis Kilintzis
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Andreas Tzavelis
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States
| | - John A Rogers
- Department of Material Science, Northwestern University, Evanston, IL, United States
| | - Aggelos K Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
| | - Nicos Maglaveras
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
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9
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Schwartz JL, Tseng E, Maruthur NM, Rouhizadeh M. Identification of Prediabetes Discussions in Unstructured Clinical Documentation: Validation of a Natural Language Processing Algorithm. JMIR Med Inform 2022; 10:e29803. [PMID: 35200154 PMCID: PMC8914791 DOI: 10.2196/29803] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 11/15/2021] [Accepted: 12/04/2021] [Indexed: 01/09/2023] Open
Abstract
Background Prediabetes affects 1 in 3 US adults. Most are not receiving evidence-based interventions, so understanding how providers discuss prediabetes with patients will inform how to improve their care. Objective This study aimed to develop a natural language processing (NLP) algorithm using machine learning techniques to identify discussions of prediabetes in narrative documentation. Methods We developed and applied a keyword search strategy to identify discussions of prediabetes in clinical documentation for patients with prediabetes. We manually reviewed matching notes to determine which represented actual prediabetes discussions. We applied 7 machine learning models against our manual annotation. Results Machine learning classifiers were able to achieve classification results that were close to human performance with up to 98% precision and recall to identify prediabetes discussions in clinical documentation. Conclusions We demonstrated that prediabetes discussions can be accurately identified using an NLP algorithm. This approach can be used to understand and identify prediabetes management practices in primary care, thereby informing interventions to improve guideline-concordant care.
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Affiliation(s)
- Jessica L Schwartz
- Division of General Internal Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States.,Division of Hospital Medicine, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Eva Tseng
- Division of General Internal Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States.,Welch Center for Prevention, Epidemiology, & Clinical Research, Johns Hopkins University, Baltimore, MD, United States
| | - Nisa M Maruthur
- Division of General Internal Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States.,Welch Center for Prevention, Epidemiology, & Clinical Research, Johns Hopkins University, Baltimore, MD, United States.,Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States
| | - Masoud Rouhizadeh
- Department of Pharmaceutical Outcomes and Policy, University of Florida College of Pharmacy, Gainesville, FL, United States.,Division of Biomedical Informatics and Data Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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10
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Sharma T, Shah M. A comprehensive review of machine learning techniques on diabetes detection. Vis Comput Ind Biomed Art 2021; 4:30. [PMID: 34862560 PMCID: PMC8642577 DOI: 10.1186/s42492-021-00097-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 10/29/2021] [Indexed: 12/14/2022] Open
Abstract
Diabetes mellitus has been an increasing concern owing to its high morbidity, and the average age of individual affected by of individual affected by this disease has now decreased to mid-twenties. Given the high prevalence, it is necessary to address with this problem effectively. Many researchers and doctors have now developed detection techniques based on artificial intelligence to better approach problems that are missed due to human errors. Data mining techniques with algorithms such as - density-based spatial clustering of applications with noise and ordering points to identify the cluster structure, the use of machine vision systems to learn data on facial images, gain better features for model training, and diagnosis via presentation of iridocyclitis for detection of the disease through iris patterns have been deployed by various practitioners. Machine learning classifiers such as support vector machines, logistic regression, and decision trees, have been comparative discussed various authors. Deep learning models such as artificial neural networks and recurrent neural networks have been considered, with primary focus on long short-term memory and convolutional neural network architectures in comparison with other machine learning models. Various parameters such as the root-mean-square error, mean absolute errors, area under curves, and graphs with varying criteria are commonly used. In this study, challenges pertaining to data inadequacy and model deployment are discussed. The future scope of such methods has also been discussed, and new methods are expected to enhance the performance of existing models, allowing them to attain greater insight into the conditions on which the prevalence of the disease depends.
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Affiliation(s)
- Toshita Sharma
- Department of Electronics and Communication Technology, Nirma University, 382481, Ahmedabad, Gujarat, India
| | - Manan Shah
- Department of Chemical Engineering, School of Technology, Pandit Deendayal Energy University, 382426, Gandhinagar, Gujarat, India.
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Lin CS, Lee YT, Fang WH, Lou YS, Kuo FC, Lee CC, Lin C. Deep Learning Algorithm for Management of Diabetes Mellitus via Electrocardiogram-Based Glycated Hemoglobin (ECG-HbA1c): A Retrospective Cohort Study. J Pers Med 2021; 11:725. [PMID: 34442369 PMCID: PMC8398464 DOI: 10.3390/jpm11080725] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/21/2021] [Accepted: 07/26/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND glycated hemoglobin (HbA1c) provides information on diabetes mellitus (DM) management. Electrocardiography (ECG) is a noninvasive test of cardiac activity that has been determined to be related to DM and its complications. This study developed a deep learning model (DLM) to estimate HbA1c via ECG. METHODS there were 104,823 ECGs with corresponding HbA1c or fasting glucose which were utilized to train a DLM for calculating ECG-HbA1c. Next, 1539 cases from outpatient departments and health examination centers provided 2190 ECGs for initial validation, and another 3293 cases with their first ECGs were employed to analyze its contributions to DM management. The primary analysis was used to distinguish patients with and without mild to severe DM, and the secondary analysis was to explore the predictive value of ECG-HbA1c for future complications, which included all-cause mortality, new-onset chronic kidney disease (CKD), and new-onset heart failure (HF). RESULTS we used a gender/age-matching strategy to train a DLM to achieve the best AUCs of 0.8255 with a sensitivity of 71.9% and specificity of 77.7% in a follow-up cohort with correlation of 0.496 and mean absolute errors of 1.230. The stratified analysis shows that DM presented in patients with fewer comorbidities was significantly more likely to be detected by ECG-HbA1c. Patients with higher ECG-HbA1c under the same Lab-HbA1c exhibited worse physical conditions. Of interest, ECG-HbA1c may contribute to the mortality (gender/age adjusted hazard ratio (HR): 1.53, 95% conference interval (CI): 1.08-2.17), new-onset CKD (HR: 1.56, 95% CI: 1.30-1.87), and new-onset HF (HR: 1.51, 95% CI: 1.13-2.01) independently of Lab-HbA1c. An additional impact of ECG-HbA1c on the risk of all-cause mortality (C-index: 0.831 to 0.835, p < 0.05), new-onset CKD (C-index: 0.735 to 0.745, p < 0.01), and new-onset HF (C-index: 0.793 to 0.796, p < 0.05) were observed in full adjustment models. CONCLUSION the ECG-HbA1c could be considered as a novel biomarker for screening DM and predicting the progression of DM and its complications.
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Affiliation(s)
- Chin-Sheng Lin
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, No 325, Section 2, Cheng-Kung Rd., Neihu, Taipei 114, Taiwan;
| | - Yung-Tsai Lee
- Division of Cardiovascular Surgery, Cheng Hsin Rehabilitation and Medical Center, No 45, Cheng Hsin St., Beitou, Taipei 112, Taiwan;
| | - Wen-Hui Fang
- Department of Family and Community Medicine, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, No 325, Section 2, Cheng-Kung Rd., Neihu, Taipei 114, Taiwan;
| | - Yu-Sheng Lou
- Graduate Institute of Life Sciences, National Defense Medical Center, No.161, Section 6, Min-Chun E. Rd., Neihu, Taipei 114, Taiwan;
- School of Public Health, National Defense Medical Center, No.161, Section 6, Min-Chun E. Rd., Neihu, Taipei 114, Taiwan
| | - Feng-Chih Kuo
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, No 325, Section 2, Cheng-Kung Rd., Neihu, Taipei 114, Taiwan;
| | - Chia-Cheng Lee
- Department of Medical Informatics, Tri-Service General Hospital, National Defense Medical Center, No 325, Section 2, Cheng-Kung Rd., Neihu, Taipei 114, Taiwan;
- Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, No 325, Section 2, Cheng-Kung Rd., Neihu, Taipei 114, Taiwan
| | - Chin Lin
- Graduate Institute of Life Sciences, National Defense Medical Center, No.161, Section 6, Min-Chun E. Rd., Neihu, Taipei 114, Taiwan;
- School of Public Health, National Defense Medical Center, No.161, Section 6, Min-Chun E. Rd., Neihu, Taipei 114, Taiwan
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, No.161, Section 6, Min-Chun E. Rd., Neihu, Taipei 114, Taiwan
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