Healthcare Data Labeling & Clinical Dataset Structuring for AI Systems
Led the design, structuring, and annotation of large-scale healthcare datasets for training and evaluating AI systems, with a focus on clinical reasoning, safety, and real-world applicability in high-acuity environments. Worked with datasets exceeding 300K+ healthcare professionals and multi-dimensional clinical data structures (40+ attributes per record), building normalized schemas and annotation pipelines to support downstream machine learning workflows. Core contributions included: • Designing structured data schemas for clinical variables (labs, vitals, medications, workflows) • Annotating and classifying healthcare data for supervised learning tasks • Performing RLHF-style evaluation of AI outputs, focusing on clinical accuracy, reasoning quality, and safety constraints • Developing evaluation rubrics for grading model responses (correctness, completeness, risk awareness) • Identifying hallucinations and unsafe recommendations in clinical AI outputs • Standardizing labeling guidelines to ensure inter-annotator consistency and high data quality • Implementing rule-based validation layers for threshold-based alerts (e.g., lab abnormalities, ICU triggers) • Preparing datasets for fine-tuning and model evaluation workflows Approach emphasized clinician-aligned labeling, ensuring outputs reflect real-world decision-making rather than purely theoretical correctness.