A Unified Big Data Architecture for AI-Driven Predictive Healthcare Analytics
Keywords:
Healthcare Predictive Analytics, Clinical Risk Prediction, Disease Progression Modeling, AI in Healthcare Analytics, ClintelliBase Architecture, Healthcare Data Integration, Predictive Model Lifecycle, Hospital Readmission Prediction, Clinical Decision Support, Healthcare Data Governance, Medical Data Security, Predictive Healthcare Systems, Risk Stratification Models, Patient Outcome Forecasting, Healthcare Data Pipelines, AI-Driven Clinical Insights, SLOPE Framework, Clinical Utility Metrics, Healthcare Analytics Platforms, Data-Driven Healthcare.Abstract
Healthcare predictive analytics involves the integration of large data volumes from heterogeneous sources together with AI algorithms to yield predictions in the form of risk levels, timelines, or progression forecasts. The ClintelliBase architecture synthesizes these components to formalize the entire predictive healthcare lifecycle and its generalization into other healthcare domains. It encompasses data ingestion, governance, compliance, security, storage, analysis, external client access, and the execution of predictive models. The proposed predictive healthcare methodology complements the formative work, empowering the ClintelliBase architecture to address predictive healthcare as a whole.
Formative work developed predictive models for hospital readmission risk and disease progression forecasting for several diseases, converging the findings with that of other works and the underlying architecture to provide a comprehensive overview. Altogether, predictive healthcare has been demarcated into a dedicated natural SLOPE space, establishing supporting elements relative to the two specific innovations on error and clinical utility.
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