Esta tarea requiere encontrar automáticamente menciones de enfermedades en casos clínicos publicados y asignar un término SNOMED CT a cada mención.
Publicación
Antonio Miranda-Escalada, Luis Gascó, Salvador Lima-López, Eulàlia Farré-Maduell, Darryl Estrada, Anastasios Nentidis, Anastasia Krithara, Georgios Katsimpras, Georgios Paliouras, Martin Krallinger (2022) Overview of DisTEMIST at BioASQ: Automatic detection and normalization of diseases from clinical texts: results, methods, evaluation and multilingual resources. Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings. http://ceur-ws.org/Vol-3180/paper-11.pdf .
Idioma
Español
NLP topic
Tarea abstracta
Dataset
Año
2022
Enlace publicación
Métrica Ranking
Macro F1
Mejores resultados para la tarea
| Sistema | Precisión | Recall | F1 | CEM | Accuracy | MacroPrecision | MacroRecall | MacroF1 Ordenar ascendente | RMSE | MicroPrecision | MicroRecall | MicroF1 | MAE | MAP | UAS | LAS | MLAS | BLEX | Pearson correlation | Spearman correlation | MeasureC | BERTScore | EMR | Exact Match | F0.5 | Hierarchical F | ICM | MeasureC | Propensity F | Reliability | Sensitivity | Sentiment Graph F1 | WAC | b2 | erde30 | sent | weighted f1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| HPI-DHC-postprocess | 0.6207 | 0.5196 | 0.5657 | ||||||||||||||||||||||||||||||||||
| Better Innovations Lab & Norwegian Centre for E-health Research-run1-snomed | 0.5478 | 0.4577 | 0.4987 | ||||||||||||||||||||||||||||||||||
| Better Innovations Lab & Norwegian Centre for E-health Research-run2-snomed-limited | 0.5497 | 0.4549 | 0.4978 | ||||||||||||||||||||||||||||||||||
| HPI-DHC-4-ensemble-reranking | 0.5427 | 0.4513 | 0.4928 | ||||||||||||||||||||||||||||||||||
| HPI-DHC-3-ensemble | 0.4678 | 0.3890 | 0.4248 |
