Key result
Deep learning using wearable and EHR data shows promise for personalized monitoring and outcome prediction.
Why the study?
Personalized health monitoring and prediction have become essential with the growing prevalence of chronic diseases and an aging population, and deep learning has emerged as a promising approach.
Deep learning offers promising approaches for personalized health monitoring and prediction using diverse data sources like wearables and electronic health records.
May support personalized monitoring via wearables and EHRs; leaves open prospective validation before clinical adoption.
Personalized health monitoring and prediction have become essential for improving health- care delivery, especially with the growing prevalence of chronic diseases and an aging population. Deep learning (DL) has emerged as a promising approach for developing personalized health mon- itoring systems that can predict health outcomes accurately and efficiently. With the increasing availability of personal health data, DL-based methods have emerged as a promising approach to improve healthcare delivery by providing accurate and timely predictions of health outcomes. This article provides a comprehensive review of the recent developments in the application of DL for personalized health monitoring and prediction. It summarizes various DL architectures and their applications for personalized health monitoring, including wearable devices, electronic health records, and social media data. Furthermore, the article also explores the challenges and future directions for the application of DL in personalized health monitoring. valuable insights into the potential of DL for personalized health monitoring and prediction.
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Rajesh et al. (2023) conducted a review in Chronic diseases. Deep learning (DL) architectures was evaluated. Deep learning architectures applied to personal health data, including wearables and electronic health records, offer a promising approach for personalized health monitoring and outcome prediction.
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