Bridging Big Data and Machine Learning for Proactive Patient Care
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 integrates large volumes of heterogeneous data with AI algorithms to generate predictions such as risk levels, timelines, and disease progression forecasts. This paper presents ClintelliBase, an architecture that formalizes the full predictive healthcare lifecycle—spanning data ingestion, governance, compliance, security, storage, analysis, external client access, and predictive model execution—while remaining generalizable across healthcare domains. The proposed methodology builds on prior formative work, extending the ClintelliBase architecture to address predictive healthcare holistically.
That earlier work developed predictive models for hospital readmission risk and disease progression across several conditions, situating these findings alongside related research and the underlying architecture to provide a comprehensive view of the field. Building on this foundation, we position predictive healthcare within a dedicated design space, introducing supporting elements around two key contributions: improved predictive error and enhanced clinical utility.
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