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Chato L, Regentova E. Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data. J Pers Med 2023; 13:1703. [PMID: 38138930 PMCID: PMC10744730 DOI: 10.3390/jpm13121703] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 12/01/2023] [Accepted: 12/08/2023] [Indexed: 12/24/2023] Open
Abstract
Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies.
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Affiliation(s)
- Lina Chato
- Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, NV 89154, USA;
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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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Liu T, Ren Z, Xiong C, Peng W, Wu J, Huang S, Liang G, Sun B. Optoacoustic classification of diabetes mellitus with the synthetic impacts via optimized neural networks. Heliyon 2023; 9:e20796. [PMID: 37842612 PMCID: PMC10569993 DOI: 10.1016/j.heliyon.2023.e20796] [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: 01/04/2023] [Revised: 09/11/2023] [Accepted: 10/06/2023] [Indexed: 10/17/2023] Open
Abstract
A highly accurate classification of diabetes mellitus (DM) with the synthetic impacts of several variables is first studied via optoacoustic technology in this work. For this purpose, an optoacoustic measurement apparatus of blood glucose is built, and the optoacoustic signals and peak-peak values for 625 cases of in vitro rabbit blood are obtained. The results show that although the single impact of five variables are obtained, the precise classification of DM is limited because of the synthetic impacts. Based on clinical standards, different levels of blood glucose corresponding to hypoglycaemia, normal, slight diabetes, moderate diabetes and severe diabetes are employed. Then, a wavelet neural network (WNN) is utilized to establish a classification model of DM severity. The classification accuracy is 94.4 % for the testing blood samples. To enhance the classification accuracy, particle swarm optimization (PSO) and quantum-behaved particle swarm optimization (QPSO) are successively utilized to optimize WNN, and accuracy is enhanced to 98.4 % and 100 %, respectively. It is demonstrated from comparison between several algorithms that optoacoustic technology united with the QPSO-optimized WNN algorithm can achieve precise classification of DM with synthetic impacts.
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Affiliation(s)
- Tao Liu
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
| | - Zhong Ren
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
- Key Laboratory of Optic-electronic Detection and Information Processing of Nanchang City, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
| | - Chengxin Xiong
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
| | - Wenping Peng
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
| | - Junli Wu
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
| | - Shuanggen Huang
- Agricultural Equipment Key Laboratory of Jiangxi Provincial, Jiangxi Agriculture University, 330045 Nanchang, Jiangxi, China
| | - Gaoqiang Liang
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
| | - Bingheng Sun
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
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Yaribeygi H, Maleki M, Jamialahmadi T, Shakhpazyan NK, Kesharwani P, Sahebkar A. Nanoparticles with SGLT2 inhibitory activity: Possible benefits and future. Diabetes Metab Syndr 2023; 17:102869. [PMID: 37778134 DOI: 10.1016/j.dsx.2023.102869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/12/2023] [Accepted: 09/21/2023] [Indexed: 10/03/2023]
Abstract
AIM Nano-drug delivery is a rapidly growing approach in medicine that helps design and develop newer forms of drugs with more efficacy and lower adverse effects. Sodium-glucose cotransporter-2 inhibitors are an emerging class of antidiabetic agents that reduce the blood glucose levels by damping glucose reabsorption in renal proximal tubules. METHODS AND RESULTS This mechanism might be followed by some adverse effects that could be prevented by nano-drug delivery. Although we have still limited evidence about nanoforms of sodium-glucose cotransporter-2 inhibitors, current knowledge strongly suggests that nanotechnology can help us design more effective drugs with lower side effects. In recent years, several studies have explored the possible benefits of nanoforms of sodium-glucose cotransporter-2 inhibitors. However, clinical trials are yet to be conducted. CONCLUSION In the current review, we present the latest findings on the development and benefits of nanoforms of sodium-glucose cotransporter-2 inhibitors.
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Affiliation(s)
- Habib Yaribeygi
- Research Center of Physiology, Semnan University of Medical Sciences, Semnan, Iran.
| | - Mina Maleki
- Urology and Nephrology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Tannaz Jamialahmadi
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Nikolay K Shakhpazyan
- Petrovsky National Science Center of Surgery, 2 Abrikosovsky Lane, 119991, Moscow, Russia
| | - Prashant Kesharwani
- Department of Pharmaceutics, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, 110062, India; Center for Global Health Research, Saveetha Medical College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, India.
| | - Amirhossein Sahebkar
- Applied Biomedical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
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Iparraguirre-Villanueva O, Espinola-Linares K, Flores Castañeda RO, Cabanillas-Carbonell M. Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes. Diagnostics (Basel) 2023; 13:2383. [PMID: 37510127 PMCID: PMC10378239 DOI: 10.3390/diagnostics13142383] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 06/23/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
Early detection of diabetes is essential to prevent serious complications in patients. The purpose of this work is to detect and classify type 2 diabetes in patients using machine learning (ML) models, and to select the most optimal model to predict the risk of diabetes. In this paper, five ML models, including K-nearest neighbor (K-NN), Bernoulli Naïve Bayes (BNB), decision tree (DT), logistic regression (LR), and support vector machine (SVM), are investigated to predict diabetic patients. A Kaggle-hosted Pima Indian dataset containing 768 patients with and without diabetes was used, including variables such as number of pregnancies the patient has had, blood glucose concentration, diastolic blood pressure, skinfold thickness, body insulin levels, body mass index (BMI), genetic background, diabetes in the family tree, age, and outcome (with/without diabetes). The results show that the K-NN and BNB models outperform the other models. The K-NN model obtained the best accuracy in detecting diabetes, with 79.6% accuracy, while the BNB model obtained 77.2% accuracy in detecting diabetes. Finally, it can be stated that the use of ML models for the early detection of diabetes is very promising.
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Hammour G, Mandic DP. An In-Ear PPG-Based Blood Glucose Monitor: A Proof-of-Concept Study. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23063319. [PMID: 36992029 PMCID: PMC10057625 DOI: 10.3390/s23063319] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/12/2023] [Accepted: 03/17/2023] [Indexed: 06/12/2023]
Abstract
Monitoring diabetes saves lives. To this end, we introduce a novel, unobtrusive, and readily deployable in-ear device for the continuous and non-invasive measurement of blood glucose levels (BGLs). The device is equipped with a low-cost commercially available pulse oximeter whose infrared wavelength (880 nm) is used for the acquisition of photoplethysmography (PPG). For rigor, we considered a full range of diabetic conditions (non-diabetic, pre-diabetic, type I diabetic, and type II diabetic). Recordings spanned nine different days, starting in the morning while fasting, up to a minimum of a two-hour period after eating a carbohydrate-rich breakfast. The BGLs from PPG were estimated using a suite of regression-based machine learning models, which were trained on characteristic features of PPG cycles pertaining to high and low BGLs. The analysis shows that, as desired, an average of 82% of the BGLs estimated from PPG lie in region A of the Clarke error grid (CEG) plot, with 100% of the estimated BGLs in the clinically acceptable CEG regions A and B. These results demonstrate the potential of the ear canal as a site for non-invasive blood glucose monitoring.
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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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Alhaddad AY, Aly H, Gad H, Al-Ali A, Sadasivuni KK, Cabibihan JJ, Malik RA. Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection. Front Bioeng Biotechnol 2022; 10:876672. [PMID: 35646863 PMCID: PMC9135106 DOI: 10.3389/fbioe.2022.876672] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 04/12/2022] [Indexed: 12/12/2022] Open
Abstract
Diabetes mellitus is characterized by elevated blood glucose levels, however patients with diabetes may also develop hypoglycemia due to treatment. There is an increasing demand for non-invasive blood glucose monitoring and trends detection amongst people with diabetes and healthy individuals, especially athletes. Wearable devices and non-invasive sensors for blood glucose monitoring have witnessed considerable advances. This review is an update on recent contributions utilizing novel sensing technologies over the past five years which include electrocardiogram, electromagnetic, bioimpedance, photoplethysmography, and acceleration measures as well as bodily fluid glucose sensors to monitor glucose and trend detection. We also review methods that use machine learning algorithms to predict blood glucose trends, especially for high risk events such as hypoglycemia. Convolutional and recurrent neural networks, support vector machines, and decision trees are examples of such machine learning algorithms. Finally, we address the key limitations and challenges of these studies and provide recommendations for future work.
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Affiliation(s)
- Ahmad Yaser Alhaddad
- Department of Mechanical and Industrial Engineering, Qatar University, Doha, Qatar
| | - Hussein Aly
- KINDI Center for Computing Research, Qatar University, Doha, Qatar
| | - Hoda Gad
- Weill Cornell Medicine - Qatar, Doha, Qatar
| | - Abdulaziz Al-Ali
- KINDI Center for Computing Research, Qatar University, Doha, Qatar
| | | | - John-John Cabibihan
- Department of Mechanical and Industrial Engineering, Qatar University, Doha, Qatar
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