Andreas Ebbehoj , Mette Ostergaard Thunbo ,Ole Emil Andersen, Michala Vilstrup Glindtvad, Adam Hulman
Abstract
Transfer learning is a form of machine learning where a pre-trained model trained on a specific task is reused as a starting point and tailored to another task in a different dataset. While transfer learning has garnered considerable attention in medical image analysis, its use for clinical non-image data is not well studied. Therefore, the objective of this scoping review was to explore the use of transfer learning for non-image data in the clinical literature.
Introduction
There is no doubt that most clinicians will use technologies integrating artificial intelligence (AI) to automate routine clinical tasks in the future. In recent years, the U.S. Food and Drug Administration has been approving an increasing number of AI-based solutions, dominated by deep learning algorithms.
Methods:
Scoping reviews identify available evidence, examine research practices, and characterize attributes related to a concept (i.e. transfer learning in our case) [12]. This format fits better with our research objective than a traditional systematic review and meta-analysis, as the latter require a more well-defined research question. During the process, we followed the ‘PRISMA for Scoping Reviews’ guidelines and the manual for conducting scoping reviews by the Joanna Briggs Institute.
Discussion
Applications of transfer learning were identified in a variety of clinical studies and demonstrated the potential in reusing models across different prediction tasks, data types, and even species. Improvements in predictive performance were especially striking when transfer learning was applied to smaller datasets, as compared to training machine learning algorithms from scratch. Image-based models seemed to have a large impact outside of computer vision and were reused for the analysis of time series, audio, and tabular data.
Acknowledgments: The authors are grateful to Anne VilsMøller (Librarian at the Royal Danish Library) for her valuable advice on the search strategy. We are also grateful for the comments of Professor Daniel R. Witte and the Epidemiology Group, Steno Diabetes Center Aarhus, Denmark, and of ZeinabSchäfer and Andreas Mathisen from the Department of Computer Science, Aarhus University, Denmark, on the first draft of the manuscript.
Citation: Ebbehoj A, Thunbo MØ, Andersen OE, Glindtvad MV, Hulman A (2022) Transfer learning for non-image data in clinical research: A scoping review. PLOS Digit Health 1(2): e0000014. https://doi.org/10.1371/journal.pdig.0000014
Editor: Matthew Chua Chin Heng, National University of Singapore, SINGAPORE
Received: November 5, 2021; Accepted: December 15, 2021; Published: February 17, 2022.
Copyright: © 2022 Ebbehoj et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data extracted from the identified articles and then presented in the Results section is available as an electronic supplement along with the code used for the analysis.
Funding: OEA, MGV and AH are employed at Steno Diabetes Center Aarhus that is partly funded by a donation from the Novo Nordisk Foundation. AE and MØT are supported by PhD scholarships from Aarhus University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
