… of MTL as compared to single-task learning, for health informatics, we show that the benefit
also … To implement the deep learning models, we use Keras (Chollet, 2015), with the Adam …

… The goal of the research is to combine Deep Learning and Machine Learning to create a …
the healthcare area. This research discusses the impact of machine learning and deep learning …

… deep learning is a promising technique for large-scale data analytics. While deep learning
… In this paper, we propose a deep learning methodology, which combines features learned …

… The study of Rehouma in [19] employed image signals for diagnosis through the
instrumentality of transfer learning for deep learning. Models used include AlexNet, VGG, ResNet, …

… deep learning … , deep learning methods are increasingly compared with each other rather
than only with a traditional machine learning method, suggesting the acceptance of deep …

… sification problems in health informatics. We experiment with three such problems: influenza
infection classification, drug usage classification and personal health mention classification, …

… methods affect the implementation of machine learning-based … deep learning-based feature
learning (DFL) framework to automatically learn compact representations from patient health …

… We sought articles that reported deep learning methodologies on temporal data representation
in structured EHR data from January 1, 2010, to August 30, 2020. We summarized and …

… of deep learning models with machine learning methods, but for each machine learning …
Healthcare Informatics (ICHI), 2015 International Conference on 2015 Oct 21; IEEE; pp. 408…

Reinforcement learning (RL) is a branch of Artificial intelligence (AI) that makes complex
decisions all by itself. Unlike traditional AI systems that passively absorb knowledge provided …