Left: Examples from HealthVer [1]; Right: Example of a claim that is supported and refuted by different evidence [2] With the rapid growth of biomedical research and the concurrent rise in misinformation, ensuring the accuracy of claims about treatment effectiveness is increasingly critical. Inaccurate or misleading information can have profound Read more…
Example from n2c2 of relation extraction [1] This work explores the effectiveness of employing Clinical BERT for Relation Extraction (RE) tasks in medical texts within an Active Learning (AL) framework. Our main objective is to optimize RE in medical texts through AL while examining the trade-offs between performance and computation Read more…
Clinical Named Entity Recognition (NER) is essential for extracting important medical insights from clinical narratives. Given the challenges in obtaining expert training datasets for real-world clinical applications related to data protection regulations and the lack of standardised entity types, this work represents a collaborative initiative aimed at building a German Read more…