Deep learning for personalized health monitoring and prediction: A review Damaševǐcius, Robertas Kumar Jagatheesaperumal, Senthil N. V. P. S. Kandala, Rajesh Hussain, Sadiq Alizadehsani, Roohallah Gorriz Sáez, Juan Manuel deep learning healthcare personalized health Personalized health monitoring and prediction are indispensable in advancing healthcare delivery, particularly amidst the escalating prevalence of chronic illnesses and the aging population. Deep learning (DL) stands out as a promising avenue for crafting personalized health monitoring systems adept at forecasting health outcomes with precision and efficiency. As personal health data becomes increasingly accessible, DL-based methodologies offer a compelling strategy for enhancing healthcare provision through accurate and timely prognostications of health conditions. This article offers a comprehensive examination of recent advancements in employing DL for personalized health monitoring and prediction. It summarizes a diverse range of DL architectures and their practical implementations across various realms, such as wearable technologies, electronic health records (EHRs), and data accumulated from social media platforms. Moreover, it elucidates the obstacles encountered and outlines future directions in leveraging DL for personalized health monitoring, thereby furnishing invaluable insights into the immense potential of DL in this domain. 2024-07-30T11:03:01Z 2024-07-30T11:03:01Z 2024-06-18 journal article Damaševǐcius, R. et. al. Computational Intelligence. 2024;40(3):e12682. [https://doi.org/10.1111/coin.12682] https://hdl.handle.net/10481/93642 10.1111/coin.12682 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Wiley Online Library