Sequential Behavior Modeling and Unsupervised Data Augmentation for Personalized Healthcare Recommendations Using AI

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Published: 2026-01-30
Keywords:
personalized healthcare, sequential behavior modeling, unsupervised data augmentation, artificial intelligence, recommendation systems, recurrent neural networks, generative adversarial networks

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Jiayu Chen
School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, Guangxi, China

Abstract

Personalized healthcare recommendations powered by artificial intelligence (AI) hold immense promise for improving patient outcomes and healthcare efficiency. This review paper examines the current landscape of sequential behavior modeling and unsupervised data augmentation techniques in the context of personalized healthcare recommendations. We delve into various methods for capturing the temporal dependencies in patient data, such as recurrent neural networks (RNNs), transformers, and Markov models, and assess their effectiveness in predicting future health events or treatment responses. Furthermore, we explore unsupervised data augmentation strategies, including generative adversarial networks (GANs), variational autoencoders (VAEs), and rule-based methods, which aim to enhance the quality and diversity of patient data, especially in scenarios with limited labeled information. The paper synthesizes existing research, compares different approaches, and identifies key challenges and opportunities in this rapidly evolving field. Finally, we discuss potential future directions for integrating sequential modeling and data augmentation techniques to advance personalized healthcare recommendations using AI. This review provides a comprehensive overview for researchers and practitioners interested in leveraging AI to improve healthcare delivery and patient well-being.

Article Details

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Vol. 2 No. 1 (2026)

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Articles

How to Cite

Sequential Behavior Modeling and Unsupervised Data Augmentation for Personalized Healthcare Recommendations Using AI. (2026). Hua Xia Xin Zhi, 2(1), 29-36. https://journals.hubblepress.com/index.php/hxxz/article/view/18

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