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…