Data Science for Healthcare

Healthcare is under pressure to do more with less. Data science and AI are now used to predict risk, reduce avoidable admissions, speed up documentation, and guide clinical decisions. The industry is rapidly being transformed by the power of data, using advanced analytics and predictive models to improve patient care and operational efficiency.

In this book, you will learn how healthcare data is captured and structured, how to clean and prepare it, and how to build predictive models for problems like sepsis risk and length of stay. The book covers natural language processing for clinical notes, computer vision for imaging, and generative AI for tasks such as question answering and denial review. It also shows how to evaluate models, monitor them in production, and design workflows that people will actually use.

By the end of this book, you will know how to move from an idea to a working healthcare AI solution. You will be able to frame the use case, choose the correct data, build and evaluate a model, explain its output, and position it in a clinical or business workflow.

WHAT YOU WILL LEARN
● Understand how healthcare data is captured, structured, and governed.
● Build predictive models for sepsis risk, readmission, and length of stay.
● Apply NLP to clinical notes for extraction, summarization, and question answering.
● Use computer vision techniques to analyze scans and imaging data.
● Leverage generative AI and RAG for clinician-facing decision support.
● Design evaluation, monitoring, and explainability for production healthcare models.
● Integrate AI outputs into real clinical and operational workflows.

WHO THIS BOOK IS FOR
This book is for anyone working at the intersection of data and healthcare, including data scientists, analysts, machine learning engineers, clinical informatics teams, and digital health leaders. It is designed for readers who want practical, working examples of AI in patient risk prediction, documentation support, and workflow automation.

1. Introduction to Data Science in Healthcare
2. Fundamentals of Healthcare Data
3. Data Preparation and Mining
4. Predictive Modeling for Patient Care
5. Image Analysis and Computer Vision in Healthcare
6. Natural Language Processing in Healthcare
7. Generative AI in Healthcare
8. Healthcare Operations Optimization
9. Ethical Considerations in Healthcare Data Science
10. Future Trends and Innovations
11. Case Studies and Real-world Applications
12. Conclusion and Call to Action

Nitin Singh is a data science professional with over 14 years of experience working at the intersection of healthcare, analytics, and artificial intelligence. His career began with data-driven problem solving at global organizations such as Amazon and Deloitte, where he built a strong grounding in large-scale data systems and business analytics. Over time, his focus shifted towards using data to improve healthcare delivery and decision-making.

At Primera Medical Technologies, a captive unit of Prime Healthcare in the United States, Nitin led projects on predictive modeling and natural language processing aimed at enhancing hospital operations and patient outcomes. Later, as part of the real-world evidence team at Sanofi, he worked on machine learning and generative AI applications that connected pharmaceutical insights with real-world clinical practice.

Academically, Nitin has an engineering background and is an alumnus of the Indian School of Business, where he completed the advanced management program in business analytics. He is currently pursuing a PhD at the Indian Institute of Technology, Patna in the field of artificial intelligence and data science.

His work reflects a deep belief that data science achieves its highest value when used with empathy and purpose. Through his research and writing, he continues to explore how data and technology can make healthcare more efficient, equitable, and human-centered.