Reactive PublishingAI for Precision Medicine with Python bridges the gap between raw patient data and truly personalized care. This book shows how modern AI techniques can be applied to real-world clinical and biomedical data to move beyond one-size-fits-all medicine and toward individualized diagnosis, risk prediction, and treatment planning.
Designed for data scientists, biomedical researchers, clinicians, and advanced practitioners, the book walks through how to build end-to-end precision medicine pipelines using Python. You'll learn how to integrate multi-modal patient data including genomics, medical imaging, electronic health records, lab results, and wearable data, and transform them into actionable, patient-specific models.
Rather than focusing on theory alone, this book emphasizes practical implementation. You'll work with real architectures and workflows for feature engineering across heterogeneous data sources, training machine learning and deep learning models, handling missing and biased clinical data, and validating models in healthcare-constrained environments. Topics include multi-modal fusion strategies, survival analysis, disease subtyping, treatment response prediction, and explainable AI techniques critical for clinical trust and regulatory compliance.
Throughout the book, ethical considerations, data privacy, and deployment challenges are treated as first-class problems, not afterthoughts. You'll learn how to design models that are interpretable, auditable, and aligned with real clinical decision-making.
AI for Precision Medicine with Python is not a high-level overview. It is a hands-on guide to building personalized medicine systems that reflect how healthcare data actually behaves in practice.
If you want to move from generic medical AI models to truly patient-centered intelligence, this book provides the tools, frameworks, and mindset to do it right.
