Big Data in Oncology: Impact, Challenges, and Risk Assessment (River Publishers Series in Biomedical Engineering) (Original Publisher)

Big Data in Oncology: Impact, Challenges, and Risk Assessment offers an in-depth exploration of the intersection between oncology and data science. As cancer diagnosis and treatment become increasingly data-intensive, the need for reliable, scalable, and ethical data-driven solutions is greater than ever. This book serves as a critical resource for biomedical engineers, oncologists, clinical data analysts, healthcare policymakers, and postgraduate students in health informatics or computational medicine. It bridges theory and practice, empowering readers to apply big data techniques to real-world oncology challenges.

Key Features and Highlights:

• Published in 2023 by River Publishers, known for authoritative texts in biomedical engineering
• Explores the full spectrum of big data applications in oncology, including diagnostics, treatment planning, and risk modeling
• Analyzes technical and ethical challenges, including data integration, privacy, and bias in machine learning models
• Covers AI and machine learning techniques used in cancer prognosis and therapy optimization
• Provides real-world case studies and risk assessment frameworks for data-driven decision-making in oncology
• Offers insights into the future of precision medicine, data governance, and clinical interoperability

Topics Covered:
Although exact chapter titles are not listed, the book includes core chapters on:

• Big data analytics in cancer detection and diagnosis
• Machine learning models in oncology decision support
• Risk assessment and data integrity in clinical oncology
• Data integration from genomics, imaging, and EHRs
• Regulatory and ethical considerations in oncological data science

About the Author(s):
Authored by a team of interdisciplinary experts in oncology, biomedical engineering, and health informatics. The contributors combine academic excellence with clinical and industry experience in cancer data modeling and analytics.

Format & Technical Details:

• Available Formats: PDF
• File Size: Approximately 10–16 MB
• Language: English
• Device Compatibility: Fully compatible with Kindle, Apple Books, Android, iOS, desktop (Windows/macOS), and all major eReader platforms

Frequently Asked Questions

Q1: Is this book suitable for readers without a background in computer science?
A1: Yes. While it covers advanced data analytics, the content is structured to be accessible to medical professionals and researchers with a foundational understanding of oncology or biomedical sciences.

Q2: Does the book provide practical examples or case studies?
A2: Absolutely. The book includes real-world case studies and applications of big data in oncology, helping bridge the gap between theory and clinical implementation.