In recent years, machine learning (ML) and deep learning (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given the current progress in the fields of ML...
This survey provides an overview of various approaches to achieve higher interpretability in reinforcement learning (RL). To that aim, we distinguish interpretability (as an intrinsic property of a model) and explainability (as a post-hoc operation) and discuss them in the context of RL with an...
Purpose: The purpose of this paper is to provide a comprehensive review on the academic journey of machine and deep learning in smart healthcare and to highlight the challenges and opportunities in adopting these approaches in smart healthcare system. Design/methodology/approach: The authors conducte...
As a result, new approaches are needed to attack the problem. Machine learning (ML) synthetic data-driven models, including variational autoencoders34, generative adversarial networks (GANs)35and, more recently, diffusion models (DMs)36, have exhibited remarkable success across diverse fields such a...
To realize the true benefits of AI and machine learning in healthcare and create a better patient-centered health future, clinicians and physicians must increasingly adopt business leadership roles to champion, integrate, and overcome the challenges of implementation. The drive to overcome integration...
One of the most powerful approaches in machine learning are ensemble methods. An ensemble uses multiple learning algorithms to obtain a final predictive performance that is often better than the performance obtainable from any of the constituent learning algorithms alone. The random forest algorithm14 ...
By combining machine learning approaches with clinical logic we can ensure patient safety while driving a positive change in antimicrobial utilisation. In the future we will conduct further research on how such solutions could be combined and implemented in real-time to create a complete CDSS for ...
• Hardware-software design approaches for smart edge processing• Real-time and safety-critical smart edge sensors for industrial IoT• AI-based human-computer interaction• QoS/QoE optimization in IoT-based systems• Re-enforcement learning for human healthcare• Heterogeneous systems-on-...
In line with this broader trend, a growing body of work has shown the potential of predicting student dropout with the help of machine learning. In contrast to traditional inferential approaches, machine learning approaches are predominantly concerned with predictive performance (i.e., the ability ...
A review on deep learning approaches in healthcare systems: taxonomies, challenges, and open issues. J. Biomed. Inform. 113, 103627 (2021). Article Google Scholar Kiran, B. R. et al. Deep reinforcement learning for autonomous driving: a survey. IEEE Trans. Intell. Transp. Syst. 23, ...