Papers by Alexander Loeser
DDxGym: Online Transformer Policies in a Knowledge Graph Based Natural Language Environment (2024.lrec-main)
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Benjamin Winter, Alexei Gustavo Figueroa Rosero, Alexander Loeser, Felix Alexander Gers, Nancy Katerina Figueroa Rosero, Ralf Krestel
| Challenge: | specialized OpenAI Gym environment for clinical differential diagnosis is limited by data access due to privacy concerns. |
| Approach: | They propose a specialized OpenAI Gym environment for clinical differential diagnosis . they frame the task as a natural-language-based reinforcement learning problem . |
| Outcome: | The proposed model improves over baselines and improves on existing models. |
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 . |
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. |
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. |
Is Language Modeling Enough? Evaluating Effective Embedding Combinations (2020.lrec-1)
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Rudolf Schneider, Tom Oberhauser, Paul Grundmann, Felix Alexander Gers, Alexander Loeser, Steffen Staab
| Challenge: | specialized embeddings are not available for tasks like entity linking or paragraph classification. |
| Approach: | They evaluate whether universal embeddings can be complemented by specialized embeddables. |
| Outcome: | The proposed embeddings outperform state-of-the-art embeddables without any fine-tuning. |