Introduction to Computational Health Informatics

This class-tested textbook is designed for a semester-long graduate or senior undergraduate course on Computational Health Informatics. The focus of the book is on computational techniques that are widely used in health data analysis and health informatics and it integrates computer science and clinical perspectives. This book prepares computer science students for careers in computational health informatics and medical data analysis. Features - Integrates computer science and clinical perspectives - Describes various statistical and artificial intelligence techniques, including machine learning techniques such as clustering of temporal data, regression analysis, neural networks, HMM, decision trees, SVM, and data mining, all of which are techniques used widely used in health-data analysis - Describes computational techniques such as multidimensional and multimedia data representation and retrieval, ontology, patient-data deidentification, temporal data analysis, heterogeneous databases, medical image analysis and transmission, biosignal analysis, pervasive healthcare, automated text-analysis, health-vocabulary knowledgebases and medical information-exchange - Includes bioinformatics and pharmacokinetics techniques and their applications to vaccine and drug development

Arvind Kumar Bansal is a full professor of Computer Science at Kent State University, Kent, Ohio, USA. He received his PhD (1988) from Case Western Reserve University, Cleveland, Ohio, USA. His research publications and undergraduate and graduate teaching are in the fields of artificial intelligence, multimedia systems and languages, bioinformatics, and computational health informatics. Javed Iqbal Khan is a full professor of Computer Science at Kent State University, Kent, Ohio, USA. He received his PhD (1995) from the University of Hawaii at Manoa, USA. His research publications and undergraduate and graduate teachings are in the fields of artificial intelligence, computer networking protocols, educational networks, medical image processing and communication, perceptual enhancement, and automated knowledge acquisition. He has been a long-term Fulbright area expert. S. Kaisar Alam received his PhD (1996) in Electrical Engineering from the University of Rochester, New York, USA. His research publications and teaching have been primarily in signal/image processing with applications to medical imaging. He was a Principal Investigator at Riverside Research, the Chief Research Officer at a Singapore tech startup, and a visiting/adjunct faculty at two New Jersey universities. Dr. Alam has been a Fulbright Scholar and he currently runs his own consulting company specializing in medical image analysis and diagnostic and therapeutic applications of ultrasound.

Preface Chapter Outlines Classroom Use of this Textbook Acknowledgments About the Authors 1 Introduction 2 Fundamentals 3 Intelligent Data Analysis Techniques 4 Healthcare Data Organization 5 Medical Imaging Informatics 6 DICOM - Medical Image Communication 7 Bioelectric and Biomagnetic Signal Analysis 8 Clinical Data Analytics 9 Pervasive Health and Remote Care 10 Disease Prediction and Drug Development 11 End-User's Emotion and Satisfaction Contributed by Leon Sterling 12 Conclusion Appendix I: Websites for Healthcare Standards Appendix II: Healthcare-Related Conferences and Journals Appendix III: Health Informatics Related Organizations Appendix IV: Health Informatics Database Resources Appendix V: Selected Companies in Healthcare Industry Index