The book discusses how augmented intelligence can increase the efficiency and speed of diagnosis in healthcare organizations. The concept of augmented intelligence can reflect the enhanced capabilities of human decision-making in clinical settings when augmented with computation systems and methods. It includes real-life case studies highlighting impact of augmented intelligence in health care. The book offers a guided tour of computational intelligence algorithms, architecture design, and applications of learning in healthcare challenges. It presents a variety of techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. It also presents specific applications of augmented intelligence in health care, and architectural models and frameworks-based augmented solutions.
Chapter 1. A bibliometric analysis on the role of artificial intelligence in healthcare.- Chapter 2. Supervised Intelligent Clinical Approach for Breast Cancer Tumour Categorisation.- Chapter 3. Health Monitoring and Integrated Wearables.- Chapter 4. A Comprehensive Review Analysis of Alzheimer Disorder using Machine Learning Approach.- Chapter 5. Machine Learning Techniques in Medical Image: A Short Review.- Chapter 6. Analysis of Diabetic Retinopathy Detection Techniques using CNN Models.- Chapter 7. Experimental Evaluation Of Brain Tumor Image Segmentation and Detection Using CNN Model.- Chapter 8. Effective Deep Learning Algorithms for Personalized Healthcare Services.- Chapter 9. Automatic lung carcinoma identification and classification in CT images using CNN deep learning model.- Chapter 10. Augmented Intelligence: Deep Learning Models for Healthcare.- Chapter 11. Sentiment analysis and emotion detection with healthcare perspective.- Chapter 12. Augmented Intelligence in Mentalhealthcare: Sentiment analysis & emotion detection with healthcare perspective.- Chapter 13. NLP applicationsl3(
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