Multimodal Data Fusion in Healthcare

Name: Multimodal Data Fusion in Healthcare
Price: 175 USD
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ISBN: 9780443440250

AI Approaches for Precision Diagnosis

by Akansha Singh, Anuradha Dhull, Monika Lamba, Krishna Kant Singh

1st Edition

Publisher: Academic Press

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Book Details

Print ISBN	9780443440250
eText ISBN	9780443440267
Publisher	Academic Press
Publishing Year	2026
Edition	1st Edition
Language	English

Multimodal Data Fusion in Healthcare: AI Approaches for Precision Diagnosis, 1st Edition, edited by Akansha Singh, Anuradha Dhull, Monika Lamba, and Krishna Kant Singh, offers a reference on artificial intelligence methods in modern diagnostics. The text presents computational structures designed to unify diverse medical measurements into diagnostic systems.

The volume examines the synthesis of electronic health records, genomic profiles, diagnostic imaging, and wearable sensor streams. It outlines analytical frameworks for real-time patient monitoring, alongside models created to combine clinical datasets for precision medicine practices.

Specific sections address operational hurdles in clinical environments, focusing on data privacy regulations, system interoperability, and computational constraints. These chapters support implementation efforts across computational health and diagnostic engineering teams.

Chapter 1: Introduction to multimodal data fusion in healthcare
Chapter 2: DeepSeek and multimodal AI in healthcare: enabling precision diagnosis and smart clinical decision support
Chapter 3: From data to diagnosis: enhancing medical predictions with explainable AI
Chapter 4: Multimodal data fusion healthcare applications with Internet of Things
Chapter 5: Integration of natural language processing with electronic health records
Chapter 6: The neurological weather forecast: predicting cognitive storms before they arise
Chapter 7: AI–driven models for early detection and prevention of cardiovascular diseases
Chapter 8: Wearable sensor and clinical data fusion for cardiovascular risk prediction
Chapter 9: ACUCARE—an analysis of multiple disease prediction system
Chapter 10: MobileSwin‑GI: a state‑of‑the‑art hybrid deep learning model for gastrointestinal disease diagnosis
Chapter 11: Accelerating rare disease detection using generative artificial intelligence, federated learning, and multimodal data integration
Chapter 12: Enhancing brain tumor diagnosis using multimodal magnetic resonance imaging and computed tomography imaging with machine learning
Chapter 13: Artificial intelligence–driven multimodal fusion for early detection of neurodegenerative diseases
Chapter 14: Multimodal data fusion with machine learning and deep learning for improved attention–deficit hyperactivity disorder diagnosis
Chapter 15: A coherent review of deep learning techniques for in‑depth analysis of electroencephalogram signals
Chapter 16: Multimodal sentiment analysis: combining bidirectional encoder representations from transformers for text and visual features
Chapter 17: Social media bigotry detection
Chapter 18: Challenges and ethics in multimodal healthcare artificial intelligence
Chapter 19: Improving tumor diagnosis and prognosis through deep learning and medical imaging
Chapter 20: Navigating risks: a deep dive into healthcare industry analysis
Chapter 21: Conclusion and future directions in multimodal data fusion in healthcare