Data Science in the Medical Field 1st Edition

Data Science in the Medical Field, 1st Edition by Seifedine Kadry and Shubham Mahajan delivers a clear, practical roadmap for applying data science to modern healthcare challenges. Ideal for clinicians, data scientists, students, and healthcare administrators, this authoritative volume captures the momentum of analytics-driven medicine.

Explore foundational concepts and real-world applications: from statistical modeling and machine learning to deep learning, time-series analysis, imaging analytics, genomics interpretation, and predictive risk scoring. The book demystifies techniques for working with electronic health records (EHR), clinical decision support, telemedicine data, and hospital operational analytics, pairing theory with case studies that illustrate measurable improvements in patient care and workflow efficiency.

Readers gain hands-on insight into data preprocessing, feature engineering, model validation, and ethical considerations such as privacy, bias, and regulatory compliance. Chapters emphasize reproducible workflows and interpretability—critical for adoption in clinical settings—while offering practical guidance on tool selection and integration with existing health IT systems.

Whether you’re advancing research in academic centers or deploying analytics in hospitals, clinics, and public-health agencies, this text equips you to translate complex biomedical data into actionable insights. Its global perspective makes it relevant for healthcare systems across North America, Europe, Asia, Africa, and Latin America.

Concise yet comprehensive, Data Science in the Medical Field is the go-to reference for bridging data science and medicine. Add it to your professional library to start transforming healthcare data into better outcomes and smarter decisions.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.