Healthcare NLP & Medical Document Classification Project
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Entity (NER) ClassificationIn my recent work on SaharaAI, I spearheaded a specialized data annotation project focused on structuring unstructured medical records and clinical notes. The core challenge in healthcare AI is that vital clinical data is often trapped in narrative text, making it difficult for machine learning models to process accurately without highly precise, domain-specific training data. To address this, I utilized a multi-layered annotation approach: Named Entity Recognition (NER): I precisely tagged complex clinical entities including diagnostic terms, anatomical locations, and pharmacological interventions, ensuring the model could distinguish subtle variations in medical terminology. Text Summarization & SFT: I curated high-quality reference summaries of dense patient histories to train large language models (LLMs) for automated clinical reporting. Evaluation & Rating: I acted as a subject matter expert to rate model outputs for clinical safety, factuality, and adherence to medical documentation standards, filtering out potential hallucinations. As a result of this structured labeling, the dataset achieved a high inter-annotator agreement (IAA) score, directly contributing to a more robust, compliant, and accurate model. For potential clients, this highlights my ability to handle sensitive, high-complexity domain data with the strict quality control required for production-ready AI.