Data-Driven Approach for Bio-medical and Healthcare

1. Front Matter

   Pages i-xiii

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2. Personal Health Record Data-Driven Integration of Heterogeneous Data

   • Snezana Savoska, Blagoj Ristevski, Vladimir Trajkovik

   Pages 1-21

3. Privacy Issues in Data-Driven Health Care

   • M. Degerli

   Pages 23-37

4. Personalizing the Patient Discharge Process and Follow-Up Using Machine Learning Algorithms, Assessment Questionnaires and Ontology Reasoning

   • Cristina Bianca Pop, Viorica Rozina Chifu

   Pages 39-62

5. Explaining Decisions of Quantum Algorithm: Patient Specific Features Explanation for Epilepsy Disease

   • Bristy Roy Chowdhury, Linkon Chowdhury

   Pages 63-81

6. Bioinformatics Study for Determination of the Binding Efficacy of Heme-Based Protein

   • Satya Narayan Sahu, Biswajit Mishra, Subrat Kumar Pattanayak

   Pages 83-96

7. Growth Trend of Swine Flu and COVID-19 Pandemic Affected Patients Using Fuzzy Cellular Automata: A Study

   • Sumita Basu, Sreeya Ghosh

   Pages 97-114

8. Data-Driven Approach Study for the Prediction and Detection of Infectious Disease Outbreak

   • Maheswata Moharana, Akalabya Bissoyi, Fahmida Khan, Subrat Kumar Pattanayak

   Pages 115-129

9. Design and Development of Interactive, Real-Time Dashboard to Understand COVID-19 Situation in Pune

   • Akshata Chaudhari, Vaibhav Mankar, Tejas Wetal, Sachin Sakhare, Priyanka More

   Pages 131-163

10. Analyzing the Impact of COVID-19 and Vaccination Using Machine Learning and ANN

    • T. Abirami, Y. Annuncia Marena, P. Jayadharshini, T. Madhuvanthi

    Pages 165-179

11. Development of Psychiatric COVID-19 CHATBOT Using Deep Learning

    • Nivedita Bhirud, Subhash Tatale, Priyanka Jain, Archis Kulkarni, Mayuresh Panchpor, N. K. Jain

    Pages 181-203

12. Advanced Mathematical Model to Measure the Severity of Any Pandemics

    • H. R. Bhapkar, Parikshit N. Mahalle, Gitanjali R. Shinde

    Pages 205-217

13. Semi-structured Patient Data in Electronic Health Record

    • Rita Ganguly, Sayan Chakraborty

    Pages 219-233

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.

• Data-driven Healthcare
• Medical Imaging
• Internet of Medical Things
• Predictive Model
• Machine Learning
• Advanced Mathematical Model
• Computational Studies

• Department of Computer Science and Engineering, Techno International New Town, Kolkata, India

  Nilanjan Dey

Nilanjan Dey is an Associate Professor in the Department of Computer Science and Engineering, Techno International New Town, Kolkata, India. He is a visiting fellow of the University of Reading, UK. He also holds a position of Adjunct Professor at Ton Duc Thang University, Ho Chi Minh City, Vietnam. Previously, he held an honorary position of Visiting Scientist at Global Biomedical Technologies Inc., CA, USA (2012–2015). He was awarded his PhD from Jadavpur University in 2015. He is the Editor-in-Chief of the International Journal of Ambient Computing and Intelligence , IGI Global, USA. He is the Series Co-Editor of  Springer Tracts in Nature-Inspired Computing (Springer Nature), Data-Intensive Research(Springer Nature),  Advances in Ubiquitous Sensing Applications for Healthcare (Elsevier) etc. He is an associate editor of  IET Image Processing and editorial board member of Complex & Intelligent Systems, Springer Nature, Applied Soft Computing, Elsevierand a Senior member of IEEE.

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