Federated and distributed learning applications for electronic health records and structured medical data: A scoping review

by   Siqi Li, et al.

Federated learning (FL) has gained popularity in clinical research in recent years to facilitate privacy-preserving collaboration. Structured data, one of the most prevalent forms of clinical data, has experienced significant growth in volume concurrently, notably with the widespread adoption of electronic health records in clinical practice. This review examines FL applications on structured medical data, identifies contemporary limitations and discusses potential innovations. We searched five databases, SCOPUS, MEDLINE, Web of Science, Embase, and CINAHL, to identify articles that applied FL to structured medical data and reported results following the PRISMA guidelines. Each selected publication was evaluated from three primary perspectives, including data quality, modeling strategies, and FL frameworks. Out of the 1160 papers screened, 34 met the inclusion criteria, with each article consisting of one or more studies that used FL to handle structured clinical/medical data. Of these, 24 utilized data acquired from electronic health records, with clinical predictions and association studies being the most common clinical research tasks that FL was applied to. Only one article exclusively explored the vertical FL setting, while the remaining 33 explored the horizontal FL setting, with only 14 discussing comparisons between single-site (local) and FL (global) analysis. The existing FL applications on structured medical data lack sufficient evaluations of clinically meaningful benefits, particularly when compared to single-site analyses. Therefore, it is crucial for future FL applications to prioritize clinical motivations and develop designs and methodologies that can effectively support and aid clinical practice and research.


Towards the Practical Utility of Federated Learning in the Medical Domain

Federated learning (FL) is an active area of research. One of the most s...

Vertical Federated Learning: A Structured Literature Review

Federated Learning (FL) has emerged as a promising distributed learning ...

A Comprehensive Survey on Federated Learning: Concept and Applications

This paper provides a comprehensive study of Federated Learning (FL) wit...

Multi-Site Clinical Federated Learning using Recursive and Attentive Models and NVFlare

The prodigious growth of digital health data has precipitated a mounting...

Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies

Objective: Temporal electronic health records (EHRs) can be a wealth of ...

System Optimization in Synchronous Federated Training: A Survey

The unprecedented demand for collaborative machine learning in a privacy...

A survey of automatic de-identification of longitudinal clinical narratives

Use of medical data, also known as electronic health records, in researc...

Please sign up or login with your details

Forgot password? Click here to reset