Attention on Personalized Clinical Decision Support System: Federated Learning Approach

Thwal, Chu Myaet; Thar, Kyi; Tun, Ye Lin; Hong, Choong Seon

doi:10.1109/bigcomp51126.2021.00035

Cited by 12 publications

(8 citation statements)

References 13 publications

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“…The use of free text in studies was primarily associated with natural language processing, as evidenced by six studies. These investigations encompassed a range of applications: a violence risk assessment [ 57 ], benchmarking bidirectional encoder representations from transformers (BERT) models [ 58 ], a named entity recognition task [ 59 ], detecting adverse events related to vaccines [ 60 ], developing a medical relation extraction model [ 61 ], and creating a deep learning-based personalized clinical decision support system [ 62 ].…”

Section: Resultsmentioning

confidence: 99%

Survey of Medical Applications of Federated Learning

Choi,

Cha,

Lee

et al. 2024

Healthc Inform Res

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Objectives: Medical artificial intelligence (AI) has recently attracted considerable attention. However, training medical AI models is challenging due to privacy-protection regulations. Among the proposed solutions, federated learning (FL) stands out. FL involves transmitting only model parameters without sharing the original data, making it particularly suitable for the medical field, where data privacy is paramount. This study reviews the application of FL in the medical domain.Methods: We conducted a literature search using the keywords "federated learning" in combination with "medical," "healthcare," or "clinical" on Google Scholar and PubMed. After reviewing titles and abstracts, 58 papers were selected for analysis. These FL studies were categorized based on the types of data used, the target disease, the use of open datasets, the local model of FL, and the neural network model. We also examined issues related to heterogeneity and security.Results: In the investigated FL studies, the most commonly used data type was image data, and the most studied target diseases were cancer and COVID-19. The majority of studies utilized open datasets. Furthermore, 72% of the FL articles addressed heterogeneity issues, while 50% discussed security concerns.Conclusions: FL in the medical domain appears to be in its early stages, with most research using open data and focusing on specific data types and diseases for performance verification purposes. Nonetheless, medical FL research is anticipated to be increasingly applied and to become a vital component of multi-institutional research.

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Section: Resultsmentioning

confidence: 99%

Survey of Medical Applications of Federated Learning

Choi,

Cha,

Lee

et al. 2024

Healthc Inform Res

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“…In the context of electronic health records, for example, FL helps to represent and find clinically similar patients [34], as well as predict hospitalizations due to cardiac events [35]. A novel FL-based clinical decision support system can be found in [36], in which the authors have integrated FL, Recurrent Neural Networks (RNN)-based models, and attention mechanisms in order to provide accurate solutions. The goal of this system is to assist healthcare professionals in medical diagnosing and overcome privacy concerns for sharing sensitive data.…”

Section: Fl For Internet Of Medical Thingsmentioning

confidence: 99%

Handling Privacy-Sensitive Medical Data With Federated Learning: Challenges and Future Directions

Aouedi

Sacco

Piamrat

et al. 2023

IEEE J. Biomed. Health Inform.

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Recent medical applications are largely dominated by the application of Machine Learning (ML) models to assist expert decisions, leading to disruptive innovations in radiology, pathology, genomics, and hence modern healthcare systems in general. Despite the profitable usage of AI-based algorithms, these data-driven methods are facing issues such as the scarcity and privacy of user data, as well as the difficulty of institutions exchanging medical information. With insufficient data, ML is prevented from reaching its full potential, which is only possible if the database consists of the full spectrum of possible anatomies, pathologies, and input data types. To solve these issues, Federated Learning (FL) appeared as a valuable approach in the medical field, allowing patient data to stay where it is generated. Since an FL setting allows many clients to collaboratively train a model while keeping training data decentralized, it can protect privacy-sensitive medical data. However, FL is still unable to deliver all its promises and meets the more stringent requirements (e.g., latency, security) of a healthcare system based on multiple Internet of Medical Things (IoMT). For example, although no data are shared among the participants by definition in FL systems, some security risks are still present and can be considered as vulnerabilities from multiple aspects. This paper sheds light upon the emerging deployment of FL, provides a broad overview of current approaches and existing challenges, and outlines several directions of future work that are relevant to solving existing problems in federated healthcare, with a particular focus on security and privacy issues.

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“…Then, Li et al [58] also discussed ADDetector, a privacypreserving FL scheme to preserve the integrity of user's data. Later, Thwal et al [59] presented an FL scheme combined with a DL approach in maintaining the healthcare system and its privacy efficiently. Then, the authors in [60] designed an FL-based dynamic contract mechanism to ensure the user's efficient participation and privacy in the healthcare system.…”

Section: F Schemes and Framework Of Fl-himentioning

confidence: 99%

“…Then, the authors in [60] designed an FL-based dynamic contract mechanism to ensure the user's efficient participation and privacy in the healthcare system. Later, the authors in [61] presented an FL learning scheme combined with deep learning to mitigate the data integrity issues of the proposed scheme in [59] to preserve the data integrity and privacy in IoT-enabled healthcare systems using a secure access control mechanism.…”

Section: F Schemes and Framework Of Fl-himentioning

confidence: 99%

Adoption of Federated Learning for Healthcare Informatics: Emerging Applications and Future Directions

Patel¹,

Bhattacharya

Tanwar

et al. 2022

IEEE Access

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The smart healthcare system has improved the patients quality of life (QoL), where the records are being analyzed remotely by distributed stakeholders. It requires a voluminous exchange of data for disease prediction via the open communication channel, i.e., the Internet to train artificial intelligence (AI) models efficiently and effectively. The open nature of communication channels puts data privacy at high risk and affects the model training of collected data at centralized servers. To overcome this, an emerging concept, i.e., federated learning (FL) is a viable solution. It performs training at client nodes and aggregates their results to train the global model. The concept of local training preserves the privacy, confidentiality, and integrity of the patient's data which contributes effectively to the training process.The applicability of FL in the healthcare domain has various advantages, but it has not been explored to its extent. The existing surveys majorly focused on the role of FL in diverse applications, but there exists no detailed or comprehensive survey on FL in healthcare informatics (HI). We present a relative comparison of recent surveys with the proposed survey. To strengthen healthcare data privacy and increase the QoL of patients, we proposed an FL-based layered healthcare informatics architecture along with the case study on FLbased electronic health records (FL-EHR). We discuss the emerging FL models, and present the statistical and security challenges in FL adoption in medical setups. Thus, the review presents useful insights for both academia and healthcare practitioners to investigate FL application in HI ecosystems.

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Attention on Personalized Clinical Decision Support System: Federated Learning Approach

Cited by 12 publications

References 13 publications

Survey of Medical Applications of Federated Learning

Survey of Medical Applications of Federated Learning

Handling Privacy-Sensitive Medical Data With Federated Learning: Challenges and Future Directions

Adoption of Federated Learning for Healthcare Informatics: Emerging Applications and Future Directions

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