Papers by Felix Gers
Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration (2021.eacl-main)
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Betty van Aken, Jens-Michalis Papaioannou, Manuel Mayrdorfer, Klemens Budde, Felix Gers, Alexander Loeser
| Challenge: | Clinical decision support systems can help in situations where the patient's development is predicted based on textual data. |
| Approach: | They propose to use clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources into the models. |
| Outcome: | The proposed model improves performance against several baselines and demonstrates that it is transferable and can be used in clinical decision support systems. |
This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text (2022.aacl-main)
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Betty van Aken, Jens-Michalis Papaioannou, Marcel Naik, Georgios Eleftheriadis, Wolfgang Nejdl, Felix Gers, Alexander Loeser
| Challenge: | a novel method for diagnosis prediction from clinical text is needed in clinical practice . prototypical part networks and label-wise attention are used to make models interpretable and helpful . |
| Approach: | They propose a deep neural model that makes predictions based on parts of the text that are similar to prototypical patients. |
| Outcome: | The proposed method outperforms baseline models on two clinical datasets and provides valuable explanations for clinical decision support. |
Rethinking the Idiomaticity Decomposability Hypothesis: Evidence from Distributional Learning (2026.acl-long)
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| Challenge: | Decomposability is thought to predict syntactic flexibility, but is not attributed to distributional experience. |
| Approach: | They propose a model-internal measure of decomposability and relate it to human ratings, syntactic flexibility, and predictability while tracking idiom learning during pretraining. |
| Outcome: | The proposed model-internal measure correlates weakly with human judgments and shows a small but consistent negative relationship with syntactic flexibility. |
CliniBench: A Clinical Outcome Prediction Benchmark for Generative and Encoder-Based Language Models (2026.eacl-long)
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| Challenge: | generative large language models are being investigated for complex medical tasks, but their effectiveness in real-world clinical applications remains underexplored. |
| Approach: | They propose to compare encoder-based classifiers and generative LLMs for discharge diagnosis prediction from admission notes in a MIMIC-IV dataset. |
| Outcome: | The proposed benchmark compares encoder-based classifiers and generative LLMs for discharge diagnosis prediction from admission notes in the MIMIC-IV dataset. |
Cross-Lingual Knowledge Transfer for Clinical Phenotyping (2022.lrec-1)
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Jens-Michalis Papaioannou, Paul Grundmann, Betty van Aken, Athanasios Samaras, Ilias Kyparissidis, George Giannakoulas, Felix Gers, Alexander Loeser
| Challenge: | Current models for clinical phenotyping are limited to clinical notes written in English due to the large amount of labeled and unlabeled clinical text resources. |
| Approach: | They propose to use translation-based methods with domain-specific encoders and cross-lingual encoder plus adapters to perform this task for clinics that do not use the English language. |
| Outcome: | The proposed strategies outperform the state-of-the-art models for clinics that do not use the English language and have a small amount of in-domain data available. |
Attention Networks for Augmenting Clinical Text with Support Sets for Diagnosis Prediction (2022.coling-1)
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| Challenge: | Clinical language models may suffer from imbalanced vocabulary for describing diseases or symptoms. |
| Approach: | They propose to augment clinical text with potentially complementary diagnostic codes from prior admissions or as they emerge during differential diagnosis to improve the performance. |
| Outcome: | The proposed approach outperforms the previous state-of-the-art PubMedBERT by up 3% points. |