Papers by Amy Siu
Data Drift in Clinical Outcome Prediction from Admission Notes (2024.lrec-main)
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Paul Grundmann, Jens-Michalis Papaioannou, Tom Oberhauser, Thomas Steffek, Amy Siu, Wolfgang Nejdl, Alexander Loeser
| Challenge: | a pivotal dataset for clinical NLP research was released in 2016 . public access to such datasets is limited due to privacy and ethical concerns . |
| Approach: | They propose a novel clinical outcome prediction dataset based on MIMIC-IV . they provide initial insights into the performance of models trained on MIDIC-III . |
| Outcome: | The proposed dataset aims to probe the robustness and generalization of clinical outcome prediction models . the study focuses on challenges tied to evolving documentation standards and changing codes in the ICD taxonomy . |
KIMERA: Injecting Domain Knowledge into Vacant Transformer Heads (2022.lrec-1)
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| Challenge: | Recent studies show that transformer models lack specific domain knowledge and are under-performing in broad domains like the medical domain. |
| Approach: | They propose a method for retraining and instilling attention heads with structured domain knowledge by masking redundant attention heads. |
| Outcome: | The proposed method improves on seven datasets in the medical domain in information retrieval and clinical outcome prediction settings. |
TrainX – Named Entity Linking with Active Sampling and Bi-Encoders (2020.coling-demos)
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Tom Oberhauser, Tim Bischoff, Karl Brendel, Maluna Menke, Tobias Klatt, Amy Siu, Felix Alexander Gers, Alexander Löser
| Challenge: | Existing easyto-use annotation tools do not support entity linking, which leads to additional training costs for medical professionals. |
| Approach: | They propose a system for Named Entity Linking for medical experts . they use Flair and BERT to support annotating training data with active sampling . |
| Outcome: | The proposed system is capable of linking against large knowledge bases and supporting zero-shot cases where the linker has never seen the entity before. |