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Patrick Seeman
Mid-level Data Scientist and Game Tech Leader specializing in ML, healthcare analytics, and Unity
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Data scientist at Cleveland Clinic Taussig Cancer Institute who led a production automation to convert unstructured (and sometimes image-based) pathology reports into structured data for government reporting. Built an on-prem LangGraph + Ollama pipeline with OCR (Tesseract), spell-checking, confidence scoring, and human-audited guardrails to mitigate hallucinations and improve reliability under PHI constraints.
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Key Strengths
- Built and deployed LLM + rules-based NLP pipeline to structure free-text pathology reports
- Designed multi-layer validation/guardrails to reduce hallucinations and missing-critical-field errors
- Implemented OCR-to-LLM workflow for image-based external hospital reports (Tesseract + spell-checking)
- Experience operating under PHI constraints with on-prem/local inference for compliance
- Established human-audited evaluation process comparing model outputs to tumor registrar outputs
- Cross-functional collaboration with non-technical clinical stakeholders (tumor registrars, pathologists)
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