benchmarking-clinical-ner

To evaluate clinical NER model accuracy, you score predictions against character-offset gold spans to calculate span-level precision, recall, and F1, applying strict exact-span or relaxed overlapping-span boundary assessment.

Span matching for biomedical NLP entity evaluation compares predicted character offsets against gold annotations, using strict exact-match boundaries or relaxed overlapping boundaries to quantify precision, recall, and F1 scores.

Yes, you can generate a confusion matrix for clinical named entity recognition predictions, alongside per-label diagnostics, false-positive examples, and false-negative examples, while maintaining privacy-preserving error reporting without retaining plaintext PHI.

Yes, the evaluation harness supports OpenMed fixture files converted from formats such as CoNLL/BIO or BRAT, including documented handling for label normalization, nested spans, class imbalance, and restricted datasets.

Low F1 scores in biomedical NER models often stem from missed entities, spurious predictions, incorrect labels, or boundary mismatches, which you can diagnose by breaking down results by label and analyzing false positives and negatives.

To diagnose clinical NER boundary mismatches, apply both strict exact-span and relaxed overlapping-span scoring to isolate whether poor performance comes from incorrect boundaries, spurious predictions, or entirely missed entities.