Fast Convergence Federated Learning withAdaptive Gradient: An Application toMental Healthcare Monitoring Systemdoi:10.1007/978-3-031-65126-7_24Nowadays, there is increasing demand for mental health monitoring systems to enable disease diagnoses, such as anxiety and depression. However, the privacy ...
Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitatio
Besides the joint model, all other five methods were free from data exchange or centralization in the multicenter study setting, thus protecting the privacy of the patient health data. Statistical analysis This study aimed to analyze the effectiveness of federated deep learning on the task of chest...
Federated Learning, a new distributed interactive AI concept, is especially promising for smart healthcare since it allows numerous clients (such as Hospitals) to participate on AI training whilst maintaining data privacy. As a result, the authors investigated the application of FL in smart healthcar...
Neutrosophic Cognitive Maps for Clinical Decision Making in Mental Healthcare: A Federated Learning ApproachObbineni, Jagan M.Kandasamy, IlanthenralRamesh, MadhumithaSmarandache, FlorentinKandasamy, VasanthaNeutrosophic Sets & Systems
When combined with advanced machine learning algorithms, the big data could be useful to improve the health systems for decision-making, diagnosis, and treatment. Mental healthcare is also attracting attention, since most medical problems can be associated with mental states. Affective computing is ...
Mental stress detectionIoMTFederated learningWearable sensorsStress detection frameworkPrivacy preservedInternet of Medical Things (IoMT) can be leveraged for periodic sensing and recording of different health parameters using sensors, wireless communications, and computation platforms. Health care systems can ...
In the healthcare domain [13,14,15], congruent MFL has shown great application value by providing diagnosis assistance with distributed digital health data. Figure 2. Taxonomy of multimodal federated learning (MFL). For incongruent MFL, the clients usually hold unique or partially overlapped data...
federated learning; deep learning; artificial intelligence; healthcare; data privacy-preserving1. Introduction Deep learning technology has shown promising results in smart healthcare applications to assist medical diagnosis and treatment based on clinical data. For instance, deep learning assists cancer ...
This innovative approach harnesses the potential of artificial intelligence and machine learning for analyzing medical images, aiming to amplify healthcare systems’ diagnostic and predictive capabilities, all while safeguarding patient privacy and ensuring data security. Besides privacy preservation, FL also ...