Novel Approaches for Machine Learning in Healthcare Applications
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 31 July 2024 | Viewed by 1939
Special Issue Editor
Interests: artificial intelligence; health informatics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Conventional research covers a limited range of predictors for healthcare applications, using statistical models with an unrealistic assumption of ceteris paribus, i.e., “all the other variables staying constant”. For this reason, emerging literature employs artificial intelligence for healthcare applications. It is free from unrealistic assumptions of “all the other variables staying constant”. It delivers important values and rankings of predictors for healthcare applications (e.g., SHAP plots). Moreover, the notions of generative artificial intelligence and reinforcement learning are enjoying immense popularity now. Given a sequence of words, generative artificial intelligence generates a sequence of their probabilities based on BERT or GPT. Its astonishing performance comes from the attention mechanism (in which different input words receive different weights based on their similarity with the output word). And reinforcement learning is a branch of machine learning in which (1) the environment presents a series of rewards, (2) an agent takes a series of actions to maximize the cumulative reward in response, and (3) the environment moves to the next period with the given transition probabilities. In fact, it has been reinforcement learning that has brought the notion of artificial intelligence to worldwide popularity since the publication of a seminal article on Alpha-Go in 2016. Two revolutionary ideas behind reinforcement learning were that artificial intelligence (e.g., Alpha-Go) starts like a human player, i.e., takes a series of actions and maximizes the cumulative reward (chance of victory) from the limited information available in limited periods only, and that it moves far beyond the best human player ever based on the sheer power of big data covering all human players to date. Little examination has been conducted and more investigation is needed on these important issues. In this context, this Special Issue invites original and review articles on novel approaches for machine learning in healthcare applications.
Dr. Kwang-Sig Lee
Guest Editor
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Keywords
- machine learning
- deep learning
- explainable artificial intelligence
- SHAP
- generative artificial intelligence
- BERT
- GPT
- reinforcement learning
