Struggling to turn model mistakes into reviewer-ready insights without overclaiming causality? In this lesson, you’ll learn to frame a disciplined error analysis for clinical NLP, build a stakes-aware evaluation grid, and report SHAP/IG attributions with cautious, defensible phrasing that drives actionable fixes. You’ll see clear explanations, AMIA/ACL-aligned examples, and targeted exercises (MCQs, fill‑in‑the‑blanks, error correction) to lock in structure, language, and placement across Methods, Results, and Discussion. Expect calibrated, publishable wording you can drop into Overleaf/Word with confidence and auditability.
Excellence in Reporting Clinical NLP Pipelines: How to Describe PHI De-identification Pipeline in NLP with PrecisionStruggling to turn a complex PHI de-identification pipeline into reviewer-proof methods text? In this lesson, you’ll learn to frame scope and governance with precise, auditable language; report annotation workflows and IAA with numeric rigor; document modeling, calibration, and post-processing reproducibly; and present validation and error analysis that satisfy AMIA/ACL expectations. You’ll find clear explanations, exemplar sentences, and concise exercises to lock in phrasing and metrics—so your clinical NLP reporting reads as compliant, calibrated, and publication-ready.