When? March 20, 14.00-16.00
Where? Onsite:D2272 and via zoom
Registration: Please sign up for the seminar via this link https://forms.gle/qicTg53xmxQJtkBa7 by March 18. This is especially important if you plan to attend onsite so we can make sure there is fika for everyone.
Agenda
14.00-14.10 Welcome and practical information
14.10-14.55 Transparent Adverse Drug Event Detection in Swedish Clinical Text: Challenges in Building Information Extraction Pipelines for a Low-Resource Language – Elizaveta Kopacheva
14.55 – 15.05 Coffee break
15.05 – 15.50 TWIN4DEM: Strengthening democratic resilience through digital twins – Giangiacomo Bravo
15.50 -16.00 Sum up and plan for our upcoming seminars
Abstracts
Transparent Adverse Drug Event Detection in Swedish Clinical Text: Challenges in Building Information Extraction Pipelines for a Low-Resource Language – Elizaveta Kopacheva
Automatic detection of adverse drug events (ADEs) in clinical texts is an important task for pharmacovigilance and patient safety. For clinical decision support, transparency is essential: a useful system should not only classify whether a document mentions an ADE but also highlight the supporting evidence in the text. Achieving this typically requires a pipeline combining named entity recognition (NER), relation extraction (RE), and document classification. However, most prior work studies these components in isolation and primarily focuses on high-resource languages such as English and Chinese.
This presentation discusses the challenges of developing an end-to-end ADE detection pipeline for Swedish clinical text, a low-resource language. I will discuss how errors propagate through the NER–RE pipeline and affect overall performance. Particular attention is given to the complexity of Swedish clinical NER, including discontinuous entities, partial-word spans, and tokenization mismatches—especially when models rely on English-based tokenizers. I will present a comparison of multilingual and Swedish-specific pretrained models (including a clinically tuned model), as well as encoder-only and encoder–decoder architectures, and discuss their usability for transparent ADE detection in free text. I will also highlight remaining challenges in evaluation. The talk aims to provide practical insights into why building reliable and interpretable ADE detection systems for Swedish clinical text remains difficult and what considerations are important for future work.
TWIN4DEM: Strengthening democratic resilience through digital twins – Giangiacomo Bravo
Democracy research struggles to explain why democracies backslide and predict which countries are more vulnerable to erosion of the rule of law. TWIN4DEM, a Horizon Europe project, aims to address this issue by creating a digital twin (DT) of political systems.
DTs are data-intensive simulation models designed as virtual copies of real-world systems. TWIN4DEM focuses on detecting vulnerabilities in democratic systems due to executive aggrandizement and advising policymakers on preventive measures. The current version of the DT is conceptual and focuses on the decision-making process of agents. It is based on synthetic data and represents a “generic” model to be used ad base to implement country-specific ones. The model includes three core groups of agents: (a) members of government that initiate executive aggrandizement; (b) members of parliament; and (c) members of constitutional or administrative courts. Such agents interact with two main types of influencers who shape their behavior: citizens (including context-specific interest groups) and EU institutions.
The final goal of the project goal is to implement four specific country cases — Czech Republic, France, Hungary, and The Netherlands — reflecting the specificity of the different political systems and informed by local data. The first of these cases (Hungary) is planned to be developed during the spring and some preliminary results will be shared during the seminar.
