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Development of Scalable Machine Learning Framework for Health Equity Analytics
  1. case
  2. Development of Scalable Machine Learning Framework for Health Equity Analytics

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Development of Scalable Machine Learning Framework for Health Equity Analytics

appsilon.com
Medical
Non-profit
Information technology

Challenges in Health Equity Analysis

Existing analytics tools lacked advanced machine learning capabilities for predictive modeling, limited integration of social/environmental determinants of health, and inability to scale across multiple common data models. Traditional approaches like Tableau failed to provide patient-level explanations and neighborhood-specific insights required for equitable care improvements.

About the Client

Academic medical center focused on equitable patient care and population health research through innovative data analytics

Strategic Objectives

  • Develop extensible ML framework supporting multiple common data models
  • Integrate social/environmental determinants with clinical data for comprehensive analysis
  • Create explainable AI models for patient stratification and decision support
  • Enable geographic analysis of health outcomes through place-based visualization

Core System Capabilities

  • Interactive feature selection module with 1000+ variable analysis
  • Supervised/unsupervised learning pipelines (logistic regression, KMeans, decision trees)
  • Patient group clustering with SHAP-based explainability
  • GIS mapping for neighborhood-specific health outcome visualization
  • OMOP CDM integration with multi-model scalability

Technology Stack

Python (Jupyter Notebooks)
R/Shiny for web interface
SHAP for model interpretability
Principal Component Analysis (PCA)
OMOP Common Data Model

System Integrations

  • Electronic Health Record (EHR) systems
  • Geographic Information Systems (GIS)
  • Multi-institutional data repositories
  • Existing Tableau visualization infrastructure

Operational Requirements

  • Cross-institutional data model compatibility
  • Real-time predictive analytics performance
  • HIPAA-compliant data security framework
  • Scalable cloud architecture for population-level datasets
  • User-friendly interface for clinical researchers

Expected Outcomes

Enables precision medicine through predictive patient stratification, improves resource allocation via neighborhood-specific insights, reduces health disparities through social determinant analysis, and establishes scalable analytics framework for multi-site collaboration. The ML-driven approach will accelerate discovery of equity-focused interventions while maintaining regulatory compliance and clinical interpretability.

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