Description
This book provides a comprehensive guide to applying AI/ML in healthcare insurance, from understanding raw claims data to deploying risk models in production environments. It presents a data-to-decision journey—integrating scalable engineering, ethical AI practices, and predictive modeling in the healthcare insurance lifecycle. With insights from real-world U.S.-based Health tech projects, it empowers readers to design, implement, and interpret machine learning pipelines tailored for risk scoring, fraud detection, and care optimization. It covers such tools as Apache Spark, Azure, and Databricks.