BUILDING HEALTHCARE MACHINE LEARNING PIPELINES

A Practical Guide to Automating Clinical Data Workflows, Optimizing Patient Triage, and Scaling Predictive Care

von Diane B. Cray

208 Seiten, Taschenbuch

Kurzbeschreibung des Verlags

In modern healthcare, a delayed prediction isn't just an inconvenience-it can be a matter of life or death.Traditional data science playbooks and nightly batch-processing architectures collapse under the pressure of high-acuity hospital environments. If your machine learning pipeline relies on polling databases every few minutes or serving models via unoptimized Python pickling, you are building a system that reacts too late.Building Healthcare Machine Learning Pipelines is the definitive, production-grade engineering manual for data scientists, ML engineers, and software architects operating at the critical intersection of artificial intelligence and patient care. This book strips away academic abstraction to deliver battle-tested blueprints for designing, scaling, and governing real-time clinical AI systems.Whether you are integrating with enterprise Electronic Health Records (EHR) or engineering fault-tolerant streaming architectures, this book provides the roadmap from raw telemetry to life-saving production deployment.What You Will Master Inside: - Master Interoperability Standards: Deconstruct legacy hospital IT silos, parse HL7 v2 pipe-delimited feeds, and implement secure FHIR RESTful resource models (Patient, Encounter, Observation).- Engineer Real-Time Event Streams: Ditch sluggish polling scripts and build high-throughput, asynchronous Directed Acyclic Graph (DAG) telemetry processors powered by Apache Kafka.- Achieve Sub-Second Clinical Decision Support (CDS): Compile models into Open Neural Network Exchange (ONNX) graphs, leverage high-performance C++ runtimes, and eliminate cache stampedes with Redis write-through strategies.- Build High-Acuity Triage Models: Handle severe class imbalance in emergency department data, master time-aware clinical imputation without data leakage, and construct cost-sensitive Gradient Boosted Decision Trees (XGBoost).- Automate Safety & Drift Surveillance: Monitor population shifts using the Population Stability Index (PSI), deploy TreeSHAP local explanations for physician trust, and implement automated circuit breakers with graceful degradation.- Enforce Strict Compliance & Auditing: Engineer privacy by design under HIPAA and GDPR, utilize Tokenization Vaults for Protected Health Information (PHI), and generate cryptographically signed, immutable HMAC SHA-256 audit trails.Who This Book Is For: - Machine Learning Engineers looking to transition from tabular data science to production-grade clinical engineering.- HealthTech Software Architects designing low-latency, secure, and compliant cloud-native hospital systems.- Technical Leads and Engineering Managers responsible for ensuring AI model reliability, regulatory compliance, and patient safety in production.Stop treating clinical AI like standard software. Secure your copy today and build resilient, life-saving machine learning pipelines that stand up to the rigorous demands of the frontlines.

• Reihe: Independently published
• ISBN: 9798175051668
• Sprache: Englisch
• Erscheinungsdatum: 17.09.2026
• Größe: 244 x 170 mm
• Verlag: Independently published