This task focuses on the detection of sensitive text; in particular, the objective is to identify and to mask confidential data, regardless of the real type of entity or the correct identification of protected health information (PHI) type.
Publication
Montserrat Marimon, Aitor Gonzalez-Agirre, Ander Intxaurrondo, Heidy Rodríguez, Jose Lopez Martin, Marta Villegas, Martin Krallinger (2019) “Automatic De-identification of Medical Texts in Spanish: the MEDDOCAN Track, Corpus, Guidelines, Methods and Evaluation of Results.” In: IberLEF@ SEPLN. 2019, pp. 618–638.
Competition
Language
Spanish
NLP topic
Abstract task
Dataset
Year
2019
Publication link
Ranking metric
Micro F
Task results
| System | Precision | Recall | F1 | CEM | Accuracy | MacroPrecision | MacroRecall | MacroF1 | RMSE | MicroPrecision | MicroRecall | MicroF1 Sort ascending | 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| lukas.lange-1 | 0.9751 | 0.9747 | 0.9749 | ||||||||||||||||||||||||||||||||||
| lukas.lange-2 | 0.9757 | 0.9733 | 0.9745 | ||||||||||||||||||||||||||||||||||
| lukas.lange-3 | 0.9754 | 0.9735 | 0.9745 | ||||||||||||||||||||||||||||||||||
| lukas.lange-4 | 0.9752 | 0.9733 | 0.9743 | ||||||||||||||||||||||||||||||||||
| lukas.lange-5 | 0.9722 | 0.9687 | 0.9705 |
