Purpose This study aims to summarize the critical issues in medical federated learning and applicable solutions. Also, detailed explanations of how federated learning techniques can be applied to the medical field are presented. About 80 reference studies described in the field were reviewed, and the federated learning framework currently being developed by the research team is provided. This paper will help researchers to build an actual medical federated learning environment. Design/methodology/approach Since machine learning techniques emerged, more efficient analysis was possible with a large amount of data. However, data regulations have been tightened worldwide, and the usage of centralized machine learning methods has become almost infeasible. Federated learning techniques have been introduced as a solution. Even with its powerful structural advantages, there still exist unsolved challenges in federated learning in a real medical data environment. This paper aims to summarize those by category and presents possible solutions. Findings This paper provides four critical categorized issues to be aware of when applying the federated learning technique to the actual medical data environment, then provides general guidelines for building a federated learning environment as a solution. Originality/value Existing studies have dealt with issues such as heterogeneity problems in the federated learning environment itself, but those were lacking on how these issues incur problems in actual working tasks. Therefore, this paper helps researchers understand the federated learning issues through examples of actual medical machine learning environments.
While machine learning techniques are being applied to various fields for their exceptional ability to find complex relations in large datasets, the strengthening of regulations on data ownership and privacy is causing increasing difficulty in its application to medical data. In light of this, Federated Learning has recently been proposed as a solution to train on private data without breach of confidentiality. This conservation of privacy is particularly appealing in the field of healthcare, where patient data is highly confidential. However, many studies have shown that its assumption of Independent and Identically Distributed data is unrealistic for medical data. In this paper, we propose Personalized Federated Cluster Models, a hierarchical clusteringbased FL process, to predict Major Depressive Disorder severity from Heart Rate Variability. By allowing clients to receive more personalized model, we address problems caused by non-IID data, showing an accuracy increase in severity prediction. This increase in performance may be sufficient to use Personalized Federated Cluster Models in many existing Federated Learning scenarios.
Digital therapeutics, evidence-based treatments delivered through software programs, are revolutionizing healthcare by utilizing cutting-edge computing technologies. Unlike conventional medical treatment methods, digital therapeutics are based on multiple information technologies, from data collection to analysis algorithms, and treatment support approaches. In this research, we provide a comprehensive overview of the latest technologies involved in the development of digital therapeutics and highlight specific technologies necessary for the future growth of the rapidly evolving digital therapeutics market. Furthermore, we present a system design of digital therapeutics for depression, currently being developed by our research team, to provide a detailed explanation of the technical process. Digital therapeutics require various technical supports, such as collecting user data in a security-enhanced medical environment, processing and analyzing the collected data, and providing personalized treatment methods to the user. The findings from this research will enable digital therapeutic companies to enhance their product performance, consequently bolstering their market competitiveness. Additionally, the research can be further extended to explore applicable methodologies at different stages of digital therapeutic environments.
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