B!SON: A Tool for Open Access Journal Recommendation

Finding a suitable open access journal to publish scientific work is a complex task: Researchers have to navigate a constantly growing number of journals, institutional agreements with publishers, funders’ conditions and the risk of Predatory Publishers. To help with these challenges, we introduce a web-based journal recommendation system called B!SON. It is developed based on a systematic requirements analysis, built on open data, gives publisher-independent recommendations and works across domains. It suggests open access journals based on title, abstract and references provided by the user. The recommendation quality has been evaluated using a large test set of 10,000 articles. Development by two German scientific libraries ensures the longevity of the project

Interpreting patient case descriptions with biomedical language models

The advent of pre-trained language models (LMs) has enabled unprecedented advances in the Natural Language Processing (NLP) field. In this respect, various specialised LMs for the biomedical domain have been introduced, and similar to their general purpose counterparts, these models have achieved state-of-the-art results in many biomedical NLP tasks. Accordingly, it can be assumed that they can perform medical reasoning. However, given the challenging nature of the biomedical domain and the scarcity of labelled data, it is still not fully understood what type of knowledge these models encapsulate and how they can be enhanced further. This research seeks to address these questions, with a focus on the task of interpreting patient case descriptions, which provides the means to investigate the model’s ability to perform medical reasoning. In general, this task is concerned with inferring a diagnosis or recommending a treatment from a text fragment describing a set of symptoms accompanied by other information. Therefore, we started by probing pre-trained language models. For this purpose, we constructed a benchmark that is derived from an existing dataset (MedNLI). Following that, to improve the performance of LMs, we used a distant supervision strategy to identify cases that are similar to a given one. We then showed that using such similar cases can lead to better results than other strategies for augmenting the input to the LM. As a final contribution, we studied the possibility of fine-tuning biomedical LMs on PubMed abstracts that correspond to case reports. In particular, we proposed a self-supervision task which mimics the downstream tasks of inferring diagnoses and recommending treatments. The findings in this thesis indicate that the performance of the considered biomedical LMs can be improved by using methods that go beyond relying on additional manually annotated datasets