LOS prediction, HEDIS RAG concepts, care-quality analysis, and healthcare dashboard foundations.
Research Archive & Data Science Foundations
Earlier experiments in healthcare analytics, biomedical informatics, forecasting, visualization, GPU planning, and scientific ML.
These projects show the technical foundation behind the current systems portfolio: modeling notebooks, RAG concepts, visualization work, infrastructure planning, and reusable ML patterns.
Activation visualization, dimensionality reduction, model inspection, and scientific analysis patterns.
GPU planning, local inference thinking, reusable ML templates, and system-readiness notes.
ChatWithWiki AzureML RAG Demo
Azure ML Prompt Flow retrieval-augmented generation demo using Jinja prompt templates, retrieval orchestration, and public wiki-style content.
Azure MLPrompt FlowPythonJinja
Azure LOS Prediction
Length-of-stay regression work using a Microsoft toy hospital dataset, Azure Notebooks, and baseline regression methods.
PythonAzure NotebooksRegressionRandom Forest
RAG Implementation for HEDIS SSD
Healthcare quality RAG concept showing de-identification pseudocode, clinical-note preprocessing ideas, and HEDIS SSD retrieval workflow shape.
PythonspaCyRAG conceptHEDIS
HEDIS Dashboard
Early Streamlit dashboard scaffold that shows healthcare analytics UI foundations.
PythonStreamlit
Medication Recommendation Experiment
LangChain medication-recommendation concept retained as an experimental healthcare LLM example with explicit non-clinical boundaries.
PythonLangChainLLM concept
HNSC Activation Visualization
t-SNE visualization concept for final-layer activations with slide images.
Pythont-SNENumPyscikit-learn
GPU Analysis for Healthcare AI Workloads
GPU comparison notes for healthcare AI workload planning across training, inference, medical imaging, agentic systems, and multi-model orchestration.
GPU planningHealthcare AI workloadsInferenceTraining
ML Templates
Machine-learning templates repository covering reusable preprocessing, evaluation, model selection, and visualization patterns.
PythonML templatesEvaluationVisualization
Safety
Archive projects stay conservative.
Medication recommendation remains non-clinical. Forecasting projects remain historical analytics. Public examples do not include PHI, PII, secrets, or employer-confidential data.