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…
In healthcare, language models are gaining increasing attention due to their ability to automatically process large amounts of unstructured or semi-structured data. “With their emergence, our interest in understanding their capabilities for tasks such as inference with natural language as a data basis is growing,” says scientist Siting Liang, who Read more…
Figure 1. An overview of the transfer learning framework with BERT-SNER. Information extraction from clinical text has the potential for clinical research and personalized care, but annotating large data for customized requirements is prohibitive. We present a German medical Named Entity Recognition (NER) system (Liang et al., 2023) that transfers Read more…
Figure 1: The interface guides annotators in human evaluation. Snippet A displays the original oncology report with highlighted sentences extracted automatically. Snippet C lets annotators choose the model for generation, showing its architecture. The middle section (B) presents the chosen model’s generation results, with green words denoting the most learned Read more…