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Abdul Mohammed
Mid-level Data Analyst specializing in healthcare and financial analytics
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Built and productionized an LLM-powered clinical documentation and insights pipeline at Cardinal Health using LangChain + GPT-4 with RAG to summarize long clinical notes, extract medication/dosage entities, and generate structured SQL-ready outputs for downstream analytics. Emphasizes clinical reliability via labeled benchmarking (precision/recall/F1), shadow deployments, clinician human-in-the-loop review, and ongoing monitoring/orchestration with Airflow, Lambda, S3, Postgres, and Power BI.
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Key Strengths
- Built and deployed production LLM clinical documentation/insights pipeline used across multiple departments
- Strong hallucination/medical safety mitigation using RAG grounding plus rule-based validation and constrained prompts
- Rigorous evaluation methodology (labeled eval sets; precision/recall/F1 across extraction, summarization, classification)
- Shadow deployment and clinician human-in-the-loop review to validate against real clinical workflows
- End-to-end ML/LLM pipeline orchestration with Airflow (S3 ingestion through retraining to Postgres/Power BI outputs)
- Operational excellence: monitoring drift/latency/errors and improving DAG reliability with sensors, SLAs, retries/backoff
- Pragmatic model selection (fine-tuned BERT for fast extraction; GPT-4 + RAG for grounded long-text reasoning)
- Effective cross-functional collaboration with clinical managers; translates metrics into business impact and prioritizes quick wins
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