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…
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…
We present an interactive image captioning pipeline. Image captioning models take an image as input, for which they generate one or more captions, or descriptions in natural language. State-of-the-art image captioning systems are trained in an offline mode, namely with a big amount of data at once. With our approach, 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…
We implement a method for re-ranking top-10 results of a state-of-the-art question answering (QA) system. The goal of our re-ranking approach is to improve the answer selection given the user question and the top-10 candidates. We focus on improving deployed QA systems that do not allow re-training or when re-training Read more…
Crowdsourcing is recently used to automate complex tasks when computational systems alone fail. In this project, we investigate how humans can effectively contribute to automate natural language translation. The envisioned goal is a hybrid machine translation service that incrementally adapts machine translation models to new domains by employing human computation to Read more…
As medical records may cover a very long history of diseases (up to 30 years) and include a vast number of diagnoses, symptoms, results, medications, and laboratory values, we could highly benefit from advanced search capabilities in clinical information systems to allow for the retrieval of relevant data. We propose Read more…