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De Ridder D, Siddiqi MA, Dauwels J, Serdijn WA, Strydis C. NeuroDots: From Single-Target to Brain-Network Modulation: Why and What Is Needed? Neuromodulation 2024; 27:711-729. [PMID: 38639704 DOI: 10.1016/j.neurom.2024.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 11/05/2023] [Accepted: 01/10/2024] [Indexed: 04/20/2024]
Abstract
OBJECTIVES Current techniques in brain stimulation are still largely based on a phrenologic approach that a single brain target can treat a brain disorder. Nevertheless, meta-analyses of brain implants indicate an overall success rate of 50% improvement in 50% of patients, irrespective of the brain-related disorder. Thus, there is still a large margin for improvement. The goal of this manuscript is to 1) develop a general theoretical framework of brain functioning that is amenable to surgical neuromodulation, and 2) describe the engineering requirements of the next generation of implantable brain stimulators that follow from this theoretic model. MATERIALS AND METHODS A neuroscience and engineering literature review was performed to develop a universal theoretical model of brain functioning and dysfunctioning amenable to surgical neuromodulation. RESULTS Even though a single target can modulate an entire network, research in network science reveals that many brain disorders are the consequence of maladaptive interactions among multiple networks rather than a single network. Consequently, targeting the main connector hubs of those multiple interacting networks involved in a brain disorder is theoretically more beneficial. We, thus, envision next-generation network implants that will rely on distributed, multisite neuromodulation targeting correlated and anticorrelated interacting brain networks, juxtaposing alternative implant configurations, and finally providing solid recommendations for the realization of such implants. In doing so, this study pinpoints the potential shortcomings of other similar efforts in the field, which somehow fall short of the requirements. CONCLUSION The concept of network stimulation holds great promise as a universal approach for treating neurologic and psychiatric disorders.
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Affiliation(s)
- Dirk De Ridder
- Section of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand.
| | - Muhammad Ali Siddiqi
- Department of Electrical Engineering, Lahore University of Management Sciences, Lahore, Pakistan; Neuroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands; Quantum and Computer Engineering Department, Delft University of Technology, Delft, The Netherlands
| | - Justin Dauwels
- Microelectronics Department, Delft University of Technology, Delft, The Netherlands
| | - Wouter A Serdijn
- Neuroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands; Section Bioelectronics, Delft University of Technology, Delft, The Netherlands
| | - Christos Strydis
- Neuroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands; Quantum and Computer Engineering Department, Delft University of Technology, Delft, The Netherlands
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Li J, Washington P. A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study. JMIR AI 2024; 3:e52171. [PMID: 38875573 PMCID: PMC11127131 DOI: 10.2196/52171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 02/19/2024] [Accepted: 03/23/2024] [Indexed: 06/16/2024]
Abstract
BACKGROUND There are a wide range of potential adverse health effects, ranging from headaches to cardiovascular disease, associated with long-term negative emotions and chronic stress. Because many indicators of stress are imperceptible to observers, the early detection of stress remains a pressing medical need, as it can enable early intervention. Physiological signals offer a noninvasive method for monitoring affective states and are recorded by a growing number of commercially available wearables. OBJECTIVE We aim to study the differences between personalized and generalized machine learning models for 3-class emotion classification (neutral, stress, and amusement) using wearable biosignal data. METHODS We developed a neural network for the 3-class emotion classification problem using data from the Wearable Stress and Affect Detection (WESAD) data set, a multimodal data set with physiological signals from 15 participants. We compared the results between a participant-exclusive generalized, a participant-inclusive generalized, and a personalized deep learning model. RESULTS For the 3-class classification problem, our personalized model achieved an average accuracy of 95.06% and an F1-score of 91.71%; our participant-inclusive generalized model achieved an average accuracy of 66.95% and an F1-score of 42.50%; and our participant-exclusive generalized model achieved an average accuracy of 67.65% and an F1-score of 43.05%. CONCLUSIONS Our results emphasize the need for increased research in personalized emotion recognition models given that they outperform generalized models in certain contexts. We also demonstrate that personalized machine learning models for emotion classification are viable and can achieve high performance.
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Affiliation(s)
- Joe Li
- Information and Computer Sciences, University of Hawai`i at Mānoa, Honolulu, HI, United States
| | - Peter Washington
- Information and Computer Sciences, University of Hawai`i at Mānoa, Honolulu, HI, United States
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Ghadi YY, Mazhar T, Shah SFA, Haq I, Ahmad W, Ouahada K, Hamam H. Integration of federated learning with IoT for smart cities applications, challenges, and solutions. PeerJ Comput Sci 2023; 9:e1657. [PMID: 38192447 PMCID: PMC10773731 DOI: 10.7717/peerj-cs.1657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 09/29/2023] [Indexed: 01/10/2024]
Abstract
In the past few years, privacy concerns have grown, making the financial models of businesses more vulnerable to attack. In many cases, it is hard to emphasize the importance of monitoring things in real-time with data from Internet of Things (IoT) devices. The people who make the IoT devices and those who use them face big problems when they try to use Artificial Intelligence (AI) techniques in real-world applications, where data must be collected and processed at a central location. Federated learning (FL) has made a decentralized, cooperative AI system that can be used by many IoT apps that use AI. It is possible because it can train AI on IoT devices that are spread out and do not need to share data. FL allows local models to be trained on local data and share their knowledge to improve a global model. Also, shared learning allows models from all over the world to be trained using data from all over the world. This article looks at the IoT in all of its forms, including "smart" businesses, "smart" cities, "smart" transportation, and "smart" healthcare. This study looks at the safety problems that the federated learning with IoT (FL-IoT) area has brought to market. This research is needed to explore because federated learning is a new technique, and a small amount of work is done on challenges faced during integration with IoT. This research also helps in the real world in such applications where encrypted data must be sent from one place to another. Researchers and graduate students are the audience of our article.
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Affiliation(s)
- Yazeed Yasin Ghadi
- Department of Computer Science and Software Engineering, Al Ain University, Abu Dhabi, UAE
| | - Tehseen Mazhar
- Department of Computer Science, Virtual University of Pakistan, Lahore, Punjab, Pakistan
| | - Syed Faisal Abbas Shah
- Department of Computer Science, Virtual University of Pakistan, Lahore, Punjab, Pakistan
| | - Inayatul Haq
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, Henan, China
| | - Wasim Ahmad
- Department of Computer Science and Information Technology, University of Malakand, Chakdara, Dir, Pakistan
| | - Khmaies Ouahada
- School of Electrical Engineering, Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, South Africa
| | - Habib Hamam
- School of Electrical Engineering, Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, South Africa
- Commune d'Akanda, International Institute of Technology and Management, BP Libreville, Estuaire, Gabon
- Faculty of Engineering, University of Moncton, Moncton, New Brunswick, Canada
- College of Computer Science and Engineering, University of Ha'il, Ha'il, Saudi Arabia
- Production & Skills Development, Spectrum of Knowledge Production & Skills Development, Sfax, Tunisia
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Allareddy V, Rampa S, Venugopalan SR, Elnagar MH, Lee MK, Oubaidin M, Yadav S. Blockchain technology and federated machine learning for collaborative initiatives in orthodontics and craniofacial health. Orthod Craniofac Res 2023; 26 Suppl 1:118-123. [PMID: 37036565 DOI: 10.1111/ocr.12662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 03/25/2023] [Accepted: 03/29/2023] [Indexed: 04/11/2023]
Abstract
There is a paucity of largescale collaborative initiatives in orthodontics and craniofacial health. Such nationally representative projects would yield findings that are generalizable. The lack of large-scale collaborative initiatives in the field of orthodontics creates a deficiency in study outcomes that can be applied to the population at large. The objective of this study is to provide a narrative review of potential applications of blockchain technology and federated machine learning to improve collaborative care. We conducted a narrative review of articles published from 2018 to 2023 to provide a high level overview of blockchain technology, federated machine learning, remote monitoring, and genomics and how they can be leveraged together to establish a patient centered model of care. To strengthen the empirical framework for clinical decision making in healthcare, we suggest use of blockchain technology and integrating it with federated machine learning. There are several challenges to adoption of these technologies in the current healthcare ecosystem. Nevertheless, this may be an ideal time to explore how best we can integrate these technologies to deliver high quality personalized care. This article provides an overview of blockchain technology and federated machine learning and how they can be leveraged to initiate collaborative projects that will have the patient at the center of care.
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Affiliation(s)
- Veerasathpurush Allareddy
- Department of Orthodontics, University of Illinois Chicago College of Dentistry, Chicago, Illinois, USA
| | | | | | - Mohammed H Elnagar
- University of Illinois Chicago College of Dentistry, Chicago, Illinois, USA
| | - Min Kyeong Lee
- University of Illinois Chicago College of Dentistry, Chicago, Illinois, USA
| | - Maysaa Oubaidin
- University of Illinois Chicago College of Dentistry, Chicago, Illinois, USA
| | - Sumit Yadav
- UNMC College of Dentistry, Lincoln, Nebraska, USA
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Liu Y, Bi D. Quantitative risk analysis of treatment plans for patients with tumor by mining historical similar patients from electronic health records using federated learning. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2023; 43:2422-2449. [PMID: 36906293 DOI: 10.1111/risa.14124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 12/11/2022] [Accepted: 02/06/2023] [Indexed: 06/18/2023]
Abstract
The determination of a treatment plan for a target patient with tumor is a difficult problem due to the existence of heterogeneity in patients' responses, incomplete information about tumor states, and asymmetric knowledge between doctors and patients, and so on. In this paper, a method for quantitative risk analysis of treatment plans for patients with tumor is proposed. To reduce the impacts of the heterogeneity in patients' responses on analysis results, the method conducts risk analysis by mining historical similar patients from Electronic Health Records (EHRs) in multiple hospitals using federated learning (FL). For this, the Recursive Feature Elimination based on the Support Vector Machine (SVM) and Deep Learning Important FeaTures (DeepLIFT) are extended into the FL framework to select key features and determine key feature weights for identifying historical similar patients. Then, in the database of each collaborative hospital, the similarities between the target patient and all historical patients are calculated, and the historical similar patients are determined. According to the statistics of tumor states and treatment outcomes of historical similar patients in all collaborative hospitals, the related data (including the probabilities of different tumor states and possible outcomes of different treatment plans) for risk analysis of the alternative treatment plans can be obtained, which can eliminate the asymmetric knowledge between doctors and patients. The related data are valuable for the doctor and patient to make their decisions. Experimental studies have been conducted to verify the feasibility and effectiveness of the proposed method.
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Affiliation(s)
- Yang Liu
- School of Economics and Management, Dalian University of Technology, Dalian, China
| | - Donghai Bi
- School of Economics and Management, Dalian University of Technology, Dalian, China
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Issa GF, Shaalan K, Shaalan Y, Saeed HA, Fatima N, Rehman AU. Brain Stroke Prediction Using ANN. 2022 INTERNATIONAL CONFERENCE ON CYBER RESILIENCE (ICCR) 2022. [DOI: 10.1109/iccr56254.2022.9995885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Affiliation(s)
- Ghassan F. Issa
- Skyline University College, University City Sharjah,School of Information Technology,Sharjah,UAE,1797
| | - Khaled Shaalan
- The British University in Dubai,Faculty of Engineering and IT,United Arab Emirates
| | | | - Hafiza Afia Saeed
- Agriculture University,Department of Computer Science,Faisalabad,Pakistan
| | - Noor Fatima
- Fatima memorial hospital college of medicine and dentistry Lahore,Lahore,Pakistan,54000
| | - Abd Ur Rehman
- Riphah international University,Riphah School of Computing,Lahore,Pakistan
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Wang T, Du Y, Gong Y, Choo KKR, Guo Y. Applications of Federated Learning in Mobile Health: Scoping Review (Preprint). J Med Internet Res 2022; 25:e43006. [PMID: 37126398 DOI: 10.2196/43006] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 01/24/2023] [Accepted: 03/10/2023] [Indexed: 03/12/2023] Open
Abstract
BACKGROUND The proliferation of mobile health (mHealth) applications is partly driven by the advancements in sensing and communication technologies, as well as the integration of artificial intelligence techniques. Data collected from mHealth applications, for example, on sensor devices carried by patients, can be mined and analyzed using artificial intelligence-based solutions to facilitate remote and (near) real-time decision-making in health care settings. However, such data often sit in data silos, and patients are often concerned about the privacy implications of sharing their raw data. Federated learning (FL) is a potential solution, as it allows multiple data owners to collaboratively train a machine learning model without requiring access to each other's raw data. OBJECTIVE The goal of this scoping review is to gain an understanding of FL and its potential in dealing with sensitive and heterogeneous data in mHealth applications. Through this review, various stakeholders, such as health care providers, practitioners, and policy makers, can gain insight into the limitations and challenges associated with using FL in mHealth and make informed decisions when considering implementing FL-based solutions. METHODS We conducted a scoping review following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched 7 commonly used databases. The included studies were analyzed and summarized to identify the possible real-world applications and associated challenges of using FL in mHealth settings. RESULTS A total of 1095 articles were retrieved during the database search, and 26 articles that met the inclusion criteria were included in the review. The analysis of these articles revealed 2 main application areas for FL in mHealth, that is, remote monitoring and diagnostic and treatment support. More specifically, FL was found to be commonly used for monitoring self-care ability, health status, and disease progression, as well as in diagnosis and treatment support of diseases. The review also identified several challenges (eg, expensive communication, statistical heterogeneity, and system heterogeneity) and potential solutions (eg, compression schemes, model personalization, and active sampling). CONCLUSIONS This scoping review has highlighted the potential of FL as a privacy-preserving approach in mHealth applications and identified the technical limitations associated with its use. The challenges and opportunities outlined in this review can inform the research agenda for future studies in this field, to overcome these limitations and further advance the use of FL in mHealth.
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Affiliation(s)
- Tongnian Wang
- Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, United States
| | - Yan Du
- School of Nursing, The University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
| | - Yanmin Gong
- Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX, United States
| | - Kim-Kwang Raymond Choo
- Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, United States
| | - Yuanxiong Guo
- Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, United States
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Zhang L, Vashisht H, Totev A, Trinh N, Ward T. A comparison of distributed machine learning methods for the support of “many labs” collaborations in computational modeling of decision making. Front Psychol 2022; 13:943198. [PMID: 36092038 PMCID: PMC9453750 DOI: 10.3389/fpsyg.2022.943198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 07/25/2022] [Indexed: 12/03/2022] Open
Abstract
Deep learning models are powerful tools for representing the complex learning processes and decision-making strategies used by humans. Such neural network models make fewer assumptions about the underlying mechanisms thus providing experimental flexibility in terms of applicability. However, this comes at the cost of involving a larger number of parameters requiring significantly more data for effective learning. This presents practical challenges given that most cognitive experiments involve relatively small numbers of subjects. Laboratory collaborations are a natural way to increase overall dataset size. However, data sharing barriers between laboratories as necessitated by data protection regulations encourage the search for alternative methods to enable collaborative data science. Distributed learning, especially federated learning (FL), which supports the preservation of data privacy, is a promising method for addressing this issue. To verify the reliability and feasibility of applying FL to train neural networks models used in the characterization of decision making, we conducted experiments on a real-world, many-labs data pool including experiment data-sets from ten independent studies. The performance of single models trained on single laboratory data-sets was poor. This unsurprising finding supports the need for laboratory collaboration to train more reliable models. To that end we evaluated four collaborative approaches. The first approach represents conventional centralized learning (CL-based) and is the optimal approach but requires complete sharing of data which we wish to avoid. The results however establish a benchmark for the other three approaches, federated learning (FL-based), incremental learning (IL-based), and cyclic incremental learning (CIL-based). We evaluate these approaches in terms of prediction accuracy and capacity to characterize human decision-making strategies. The FL-based model achieves performance most comparable to that of the CL-based model. This indicates that FL has value in scaling data science methods to data collected in computational modeling contexts when data sharing is not convenient, practical or permissible.
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Affiliation(s)
- Lili Zhang
- School of Computing, Dublin City University, Dublin, Ireland
- Insight Science Foundation Ireland Research Centre for Data Analytics, Dublin, Ireland
- *Correspondence: Lili Zhang
| | | | - Andrey Totev
- School of Computing, Dublin City University, Dublin, Ireland
| | - Nam Trinh
- School of Computing, Dublin City University, Dublin, Ireland
| | - Tomas Ward
- School of Computing, Dublin City University, Dublin, Ireland
- Insight Science Foundation Ireland Research Centre for Data Analytics, Dublin, Ireland
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Antunes RS, da Costa CA, Küderle A, Yari IA, Eskofier B. Federated Learning for Healthcare: Systematic Review and Architecture Proposal. ACM T INTEL SYST TEC 2022. [DOI: 10.1145/3501813] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The use of machine learning (ML) with electronic health records (EHR) is growing in popularity as a means to extract knowledge that can improve the decision-making process in healthcare. Such methods require training of high-quality learning models based on diverse and comprehensive datasets, which are hard to obtain due to the sensitive nature of medical data from patients. In this context, federated learning (FL) is a methodology that enables the distributed training of machine learning models with remotely hosted datasets without the need to accumulate data and, therefore, compromise it. FL is a promising solution to improve ML-based systems, better aligning them to regulatory requirements, improving trustworthiness and data sovereignty. However, many open questions must be addressed before the use of FL becomes widespread. This article aims at presenting a systematic literature review on current research about FL in the context of EHR data for healthcare applications. Our analysis highlights the main research topics, proposed solutions, case studies, and respective ML methods. Furthermore, the article discusses a general architecture for FL applied to healthcare data based on the main insights obtained from the literature review. The collected literature corpus indicates that there is extensive research on the privacy and confidentiality aspects of training data and model sharing, which is expected given the sensitive nature of medical data. Studies also explore improvements to the aggregation mechanisms required to generate the learning model from distributed contributions and case studies with different types of medical data.
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Affiliation(s)
| | | | | | | | - Björn Eskofier
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
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A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112311191] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Recent advances in deep learning have shown many successful stories in smart healthcare applications with data-driven insight into improving clinical institutions’ quality of care. Excellent deep learning models are heavily data-driven. The more data trained, the more robust and more generalizable the performance of the deep learning model. However, pooling the medical data into centralized storage to train a robust deep learning model faces privacy, ownership, and strict regulation challenges. Federated learning resolves the previous challenges with a shared global deep learning model using a central aggregator server. At the same time, patient data remain with the local party, maintaining data anonymity and security. In this study, first, we provide a comprehensive, up-to-date review of research employing federated learning in healthcare applications. Second, we evaluate a set of recent challenges from a data-centric perspective in federated learning, such as data partitioning characteristics, data distributions, data protection mechanisms, and benchmark datasets. Finally, we point out several potential challenges and future research directions in healthcare applications.
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