Big Data and Artificial Intelligence for Healthcare Applications

Häftad, Engelska, 2024

Av Ankur Saxena, Nicolas Brault, Shazia Rashid, India) Saxena, Ankur (Amity University, Noida, Beauvais Cedex) Brault, Nicolas (Institut Polytechnique Unilasalle, India.) Rashid, Shazia (Amity University, Noida

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This book covers a wide range of topics on the role of Artificial Intelligence, Machine Learning, and Big Data for healthcare applications and deals with the ethical issues and concerns associated with it. This book explores the applications in different areas of healthcare and highlights the current research."Big Data and Artificial Intelligence for Healthcare Applications" covers healthcare big data analytics, mobile health and personalized medicine, clinical trial data management and presents how Artificial Intelligence can be used for early disease diagnosis prediction and prognosis. It also offers some case studies that describes the application of Artificial Intelligence and Machine Learning in healthcare.Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest.

Produktinformation

Utgivningsdatum2024-10-04
Mått156 x 234 x 16 mm
Vikt460 g
FormatHäftad
SpråkEngelska
SerieBig Data for Industry 4.0
Antal sidor274
FörlagTaylor & Francis Ltd
ISBN9780367554972

Tillhör följande kategorier

Medicin: allmänt inom Medicin
Artificiell intelligens inom Data och it
Databaser inom Data och it

Ankur Saxena is currently working as Assistant Professor in Amity University Uttar Pradesh (AUUP), Noida. He has 14 years of wide teaching experience at graduation and post-graduation level and 3 years of industrial experience in the field of Software Development. He has published 10 books with international reputed publication. He has published 40 research papers in reputed national and international journals. He is editorial board member and reviewer for a number of journals. His research interests are Cloud Computing, Big Data, Artificial Intelligence, Machine Learning evolutionary algorithms, software framework, design & analysis of algorithms, Biometric identification.Nicolas Brault, Associate Professor in History and Philosophy of Science, Institute Polytechnique UniLaSalle, France. He is Research Associate at SPHERE Research Unit, and Part time lecturer for Data science and Biotechnology in Society at various Schools in Paris Metropolitan. He obtained his PhD in Epistemology from SPHERE Research Unit, University Paris.Shazia Rashid is working as Assistant Professor in Amity Institute of Biotechnology (AIB) and Adjunct faculty at Amity Institute of Molecular Medicine and Stem Cells (AIMMSCR), Amity University Uttar Pradesh, Noida, India. Dr. Rashid has 10 years teaching and research experience in the area of Cancer biology and Drug Discovery. She received her Ph.D. degree in Biomedical Sciences from University of Ulster, United Kingdom after which she worked as a post-doctoral fellow at University of Ulster and later at University of Oxford, U.K. She later joined Amity University Uttar Pradesh, Noida, India and has since been involved in teaching undergraduate, post-graduate and Ph.D. students and carrying out research in the areas of women associated cancers, specially HPV infection and cervical cancer. She has published a number of research papers and book chapters in reputed international and national journals. She is strong advocate of women’s health and involved in various outreach activities involved in spreading awareness about HPV infection in women.

Part I: Conceptual. 1. Introduction to Big Data. 2. Introduction to Machine Learning. Part II: Application. 3. Machine Learning in Clinical Trials. 4. Deep Learning and Its Biological and Biomedical Applications. 5. Applications of Machine Learning Algorithms to Cancer Data. 6. Pancreatic Cancer Detection by an Integrated Level Set-Based Deep Learning Model. 7. Early and Precision-Oriented Detection of Cervical Cancer. 8. Transformation of mHealth in Society. 9. Artificial Intelligence and Deep Learning for Medical Diagnosis and Treatment. Part III: Ethics. 10. Ethical Issues and Challenges with Artificial Intelligence in Healthcare. 11. Epistemological Issues and Challenges with Artificial Intelligence in Healthcare.