Enabling Person-Centric Healthcare Using Ambient Assistive Technology, Volume 2
Description
This book focuses more on the transformative impact of person-centric health care, where it explores cutting-edge advancements in integrating artificial intelligence and machine learning to deliver personalized and efficient care. Key topics include the application of predictive models for critical health conditions such as brain stroke, lung cancer, diabetes, and Alzheimer's, as well as the integration of secure frameworks to protect sensitive patient data. The book also covers advanced techniques for recognizing human activities in ambient environments, optimizing patient data clustering, and evaluating deep learning methods for unique use cases like yoga pose classification and resource optimization in smart healthcare.
Designed for healthcare professionals, researchers, data scientists, and technologists, this book presents a harmonious blend of technical insights and practical applications, emphasizing person-centric approaches. By focusing on multi-disease prediction, assistive technologies, and enhanced emergency management, this book serves as a vital resource for innovating healthcare delivery in smart environments.
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Content
Integrating Person Centric Healthcare with Hospital Management Systems A Comprehensive Analysis for Enhanced Emergency Case Management.- Exploring Machine Learning Techniques for Predicting Brain Stroke from Heart Disease Insights from ANN, NCF, and XG Boost.- Optimizing Lung Cancer Prediction Models A Hybrid Methodology Using GWO and Random Forest.- Person Centric Healthcare A Deep Survey on the Integration of Machine Intelligence Technology.- Blockchain Driven Security for Protected Health Information in Person Centric Smart Healthcare The BHKEM Framework with Ambient Assistive Technology.- Privacy Preserving Federated Learning with Homomorphic Encryption Alzheimer's Detection Use Case.- Enhancing Person Centric Health Care for Diabetes Prediction A Comparative Study of LightGBM, XGBoost, and Hybrid LIGB Model.- Unveiling Multi Disease Prediction Using Machine Learning A Comprehensive Study.- Performance Evaluation of Different Deep Learning Models for Automatic Yoga Pose Classification.- Recognizing Human Activities in Ambient Assisted Environment from Wearable Sensor Data Using Gramian Angular Field and Deep CNN.- Detection of Hepatocellular Carcinoma Using Machine Learning and Small Set of Clinical Features.- Enhancing Patient Data Clustering in Smart Healthcare A Semi Supervised Approach for Person Centric HealthCare Treatment and Resource Optimization.- Case study to role of Large language models in prediction of the future illness.- Person Centric Care for Alzheimer's Patients A Multi Criteria Decision Support Framework for Activity Recognition.