Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. The book covers not only the best-performing methods, it also presents implementation methods. The book includes all the prerequisite methodologies in each chapter so that new researchers and practitioners will find it very useful. Chapters go from basic methodology to advanced methods, including detailed descriptions of proposed approaches and comprehensive critical discussions on experimental results and how they are applied to Biomedical Engineering, Electronic Health Records, and medical image processing.
1. Cover image - p. page_iv 2. Title page - p. page_iv 3. Table of Contents - p. page_iv 4. Copyright - p. page_iv 5. Contributors - p. page_xiii 6. 1: Unified neural architecture for drug, disease, and clinical entity recognition - p. page_xv 7. 2: Simulation on real time monitoring for user healthcare information - p. page_19 8. 3: Multimodality medical image retrieval using convolutional neural network - p. page_52 9. 4: A systematic approach for identification of tumor regions in the human brain through HARIS algorithm - p. page_95 10. 5: Development of a fuzzy decision support system to deal with uncertainties in working posture analysis using rapid upper limb assessment - p. page_118 11. 6: Short PCG classification based on deep learning - p. page_140 12. 7: Development of a laboratory medical algorithm for simultaneous detection and counting of erythrocytes and leukocytes in digital images of a blood smear - p. page_164 13. 8: Deep learning techniques for optimizing medical big data - p. page_186 14. 9: Simulation of biomedical signals and images using Monte Carlo methods for training of deep learning networks - p. page_211 15. 10: Deep learning-based histopathological image analysis for automated detection and staging of melanoma - p. page_236 16. 11: Potential proposal to improve data transmission in healthcare systems - p. page_265 17. 12: Transferable approach for cardiac disease classification using deep learning - p. page_283 18. 13: Automated neuroscience decision support framework - p. page_303 19. 14: Diabetes prediction using artificial neural network - p. page_326 20. Index - p. page_341
Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. The book covers not only the best-performing methods, it also presents implementation methods. The book includes all the prerequisite methodologies in each chapter so that new researchers and practitioners will find it very useful. Chapters go from basic methodology to advanced methods, including detailed descriptions of proposed approaches and comprehensive critical discussions on experimental results and how they are applied to Biomedical Engineering, Electronic Health Records, and medical image processing.
1. Cover image - p. page_iv 2. Title page - p. page_iv 3. Table of Contents - p. page_iv 4. Copyright - p. page_iv 5. Contributors - p. page_xiii 6. 1: Unified neural architecture for drug, disease, and clinical entity recognition - p. page_xv 7. 2: Simulation on real time monitoring for user healthcare information - p. page_19 8. 3: Multimodality medical image retrieval using convolutional neural network - p. page_52 9. 4: A systematic approach for identification of tumor regions in the human brain through HARIS algorithm - p. page_95 10. 5: Development of a fuzzy decision support system to deal with uncertainties in working posture analysis using rapid upper limb assessment - p. page_118 11. 6: Short PCG classification based on deep learning - p. page_140 12. 7: Development of a laboratory medical algorithm for simultaneous detection and counting of erythrocytes and leukocytes in digital images of a blood smear - p. page_164 13. 8: Deep learning techniques for optimizing medical big data - p. page_186 14. 9: Simulation of biomedical signals and images using Monte Carlo methods for training of deep learning networks - p. page_211 15. 10: Deep learning-based histopathological image analysis for automated detection and staging of melanoma - p. page_236 16. 11: Potential proposal to improve data transmission in healthcare systems - p. page_265 17. 12: Transferable approach for cardiac disease classification using deep learning - p. page_283 18. 13: Automated neuroscience decision support framework - p. page_303 19. 14: Diabetes prediction using artificial neural network - p. page_326 20. Index - p. page_341
