The TESTLINK task conducted at IberLEF2023 focuses on relation extraction from clinical cases in Spanish and Basque. The task consists in identifying clinical results and measures and linking them to the tests and measurements from which they were obtained. Three teams took part in the task and various (supervised) deep learning models were evaluated; interestingly, none of the teams explored the use of few-shot learning. The evaluation shows that in-domain fine-tuning and larger training datasets improve the results. In fact, the fact that supervised models significantly outperformed the baseline based on few-shot learning shows the crucial role still played by the availability of annotated training data.
Overview of TESTLINK at IberLEF 2023: Linking Results to Clinical Laboratory Tests and Measurements
Begoña Altuna;Alberto Lavelli;Bernardo Magnini;Manuela Speranza;Roberto Zanoli;Goutham Karunakaran
2023-01-01
Abstract
The TESTLINK task conducted at IberLEF2023 focuses on relation extraction from clinical cases in Spanish and Basque. The task consists in identifying clinical results and measures and linking them to the tests and measurements from which they were obtained. Three teams took part in the task and various (supervised) deep learning models were evaluated; interestingly, none of the teams explored the use of few-shot learning. The evaluation shows that in-domain fine-tuning and larger training datasets improve the results. In fact, the fact that supervised models significantly outperformed the baseline based on few-shot learning shows the crucial role still played by the availability of annotated training data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
