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AI and Machine Learning in Respiratory Data Analysis
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Author(s): S. Ida Evangeline (Government College of Engineering, Tirunelveli, India)
Copyright: 2026
Pages: 34
Source title:
Next-Generation Bioinformatics for Pulmonary Disease Research
Source Author(s)/Editor(s): Devvret Verma (Graphic Era University, India), Debasis Mitra (Graphic Era University, India), Bhavya Mudgal (Graphic Era University, India), Suraj Vitthaloo Atram (The University of Sheffield, UK)and Rokayya Sami (Taif University, Saudi Arabia)
DOI: 10.4018/979-8-3373-4923-7.ch002
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Abstract
Pulmonary diseases such as asthma, chronic obstructive pulmonary disease (COPD), interstitial lung disease, and pulmonary fibrosis present significant diagnostic and therapeutic challenges due to their clinical heterogeneity and complex pathophysiology. The rise of artificial intelligence (AI) and machine learning (ML) has ushered in a new era in respiratory medicine, enabling the integration and interpretation of vast, multimodal datasets—from imaging and spirometry to genomics and wearable sensor data. The chapter is a complete update of the changes that AI and ML are causing to the analysis of the respiratory data. It covers the principles and applications of the concept of machine learning, sophisticated deep learning algorithms in both imaging data and time-series data, and tools to integrate multiple omics and phenotype.
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