A clinical NLP pipeline that reaches 96% F1 in validation can still underperform against live hospital data, and the reason rarely traces back to the model. It traces back to...
Every framework evaluation for clinical NLP starts with an accuracy question: how well does it extract entities, detect PHI, or classify a document? That question matters, but it is the...
Risk adjustment models are only as complete as the codes they can see. A physician note describing "poorly controlled diabetes with early nephropathy and peripheral neuropathy, complicated by depression and...
RAG quality is decided before a query ever runs. See why chunking, terminology normalization, and de-identification determine whether clinical RAG retrieval is reliable. Retrieval-augmented generation lets a clinical LLM...
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