Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

Journal of Personalized Medicine · 2 authors, 1 centre

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TL;DR

It concludes that transfer learning is effective for improving model performance and efficiency in this domain.

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The survey details various transfer learning strategies, including feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning, explaining their role in addressing data quality, size, labeling, bias, privacy, and computational challenges. The review synthesizes findings from existing studies to demonstrate transfer learning's effectiveness in enhancing model accuracy and training efficiency. Limitations noted include the need for further research on adapting to real-time data streams and enabling transfer learning on edge devices.

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narrative_review

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research methods

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ORIGINAL PAPER

PMC10744730

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

Journal of Personalized Medicine

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ABSTRACT

Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies.

CITATION EXPORT

Lina Chato, Emma Regentova. Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data. Journal of Personalized Medicine. (2023). https://doi.org/10.3390/jpm13121703

AUTHORS

2 · 1 CENTRES

Lina Chato, Emma Regentova

Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, NV 89154, USA; [email protected]

EDITOR / REVIEWER

Pascale Gauthier