Healthcare Predictive Analytics Using BigQuery and TensorFlow

Abstract

Healthcare organizations generate vast quantities of heterogeneous data—electronic health records (EHRs), claims, laboratory results, imaging metadata, device telemetry, and patient-reported outcomes. Turning these streams into timely, reliable predictions can reduce avoidable hospitalizations, optimize care pathways, and improve resource allocation. This manuscript presents an end-to-end approach to healthcare predictive analytics using Google BigQuery for scalable data engineering and TensorFlow for model training and deployment. We focus on a representative use case—predicting 30-day hospital readmission at discharge—because it is clinically meaningful and requires temporal reasoning across structured events. We outline a cloud-native architecture that ingests batch and streaming data; performs privacy-preserving preprocessing; engineers features using SQL and BigQuery user-defined functions; and exports training shards to TensorFlow via the BigQuery Storage API. We compare three models: a regularized logistic regression baseline, a gradient-boosted tree model, and a sequence-aware deep neural network (LSTM-based).

The simulation study uses a large synthetic cohort with realistic class imbalance and missingness patterns to evaluate scalability, latency, and learning curves without exposing protected health information (PHI). Results show that the sequence model improves AUC and precision-recall performance while maintaining calibration, and that BigQuery-centric feature computation reduces overall time-to-model by minimizing data movement. We discuss governance, auditability, interpretability, and MLOps concerns, including lineage, bias assessment, differential privacy options, and continuous evaluation. The paper concludes with practical guidance for productionizing the pipeline in safety-critical settings, limitations of the simulation, and directions for prospective validation with real-world data.