Artificial Intelligence Algorithms in Asthma Management: A Review of Data Engineering, Predictive Models, and Future Implications
Source abstract
Abstract
Asthma is a respiratory condition affecting millions of individuals worldwide, often exacerbated by poor management and worsening weather conditions. As healthcare and weather data continue to expand, identifying the most appropriate and sustainable artificial intelligence (AI) models for asthma care has become a challenging task. Additionally, the integration of multi-modal data through advanced pre-processing and feature selection techniques has emerged as a critical innovation in developing more effective and robust models. This study examines the current state and potential of AI methods in respiratory care, utilizing available data sources to enhance outcomes. The novelty of this work highlights the progression from classical to advanced models, including machine learning, deep learning, and ChatGPT, applied to diverse data in asthma analysis, while outlining key challenges and discussing potential solutions and future directions. The aim of the study is to highlight how machine learning, deep learning, and hybrid model architectures contribute to effective asthma classification, while also demonstrating ChatGPT’s potential as a reliable support tool for physicians in asthma management and administration. It is projected that the review’s findings on key challenges and opportunities will provide insights and uncover potential research directions in asthma assessment through the application of AI models.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Learning in Healthcare · Artificial Intelligence in Healthcare and Education · Artificial Intelligence in Healthcare
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Information Technology, United Arab Emirates University, Al-Ain P.O. Box 15551, United Arab Emirates
Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-661 Warsaw, Poland
Funding
United Arab Emirates University, UAE
References (73)
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