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The book presents current research advances, both academic and industrial, in machine learning, artificial intelligence, and data analytics for biomedical and healthcare applications. The book deals with key challenges associated with biomedical data analysis including higher dimensions, class imbalances, smaller database sizes, etc. It also highlights development of novel pattern recognition and machine learning methods specific to medical and genomic data, which is extremely necessary but highly challenging. The book will be useful for healthcare professionals who have access to interesting data sources but lack the expertise to use data mining effectively.
Editor and Contributors
Personal Health Record Data-Driven Integration of Heterogeneous Data
Privacy Issues in Data-Driven Health Care
Personalizing the Patient Discharge Process and Follow-Up Using Machine Learning Algorithms, Assessment Questionnaires and Ontology Reasoning
Explaining Decisions of Quantum Algorithm: Patient Specific Features Explanation for Epilepsy Disease
Bioinformatics Study for Determination of the Binding Efficacy of Heme-Based Protein
Growth Trend of Swine Flu and COVID-19 Pandemic Affected Patients Using Fuzzy Cellular Automata: A Study
Data-Driven Approach Study for the Prediction and Detection of Infectious Disease Outbreak
Design and Development of Interactive, Real-Time Dashboard to Understand COVID-19 Situation in Pune
Analyzing the Impact of COVID-19 and Vaccination Using Machine Learning and ANN
Development of Psychiatric COVID-19 CHATBOT Using Deep Learning
Advanced Mathematical Model to Measure the Severity of Any Pandemics
Semi-structured Patient Data in Electronic Health Record