Research

Our lab develops explainable, trustworthy, and user-centric AI systems. We combine machine learning, natural language processing, and human-centered evaluation to design models that people can actually understand and rely on.

We focus on real-world settings where AI decisions have meaningful impact — from healthcare and public administration to everyday human–AI collaboration.

Explainable AI

Intrinsically interpretable models, explanation evaluation, user centric-aspects.

Language & LLMs

Capabilities, limitations, and behavior of Large Language Models.

Applied ML

Machine learning in sensitive, safety-critical, and real-world domains.

“Why does the model think this patient has stage 3 cancer?” “Why was my credit application rejected, and what can I do about it?”

Machine learning systems are used in situations where people must understand and trust automated decisions, and occasionally challenge them. In our Explainable AI (XAI) research, we design methods that make model reasoning transparent. We also analyze and improve the impact of explanations on real users.

In this area, we develop interpretable models, (Nauta et al., 2021); (Nauta et al., 2023), creates conversational agents for explanations, (Nguyen et al., 2023), examines evaluation of explanation quality, (Nauta et al., 2023); (Le et al., 2023), and studies how people perceive errors and uncertainty, (Papenmeier et al., 2022); (Papenmeier et al., 2022).

Language & LLMs

“How does a model encode meaning beneath billions of parameters?” “And how can we use it reliably in situations where mistakes matter?”

Language is messy, individualized, and deeply context-dependent. Our work investigates how large language models (LLMs) represent information, how they can be adapted or constrained, and how we can evaluate their outputs in reliable, human-centered ways.

Our research in this area examines LLM representations and probing, (Youssef et al., 2023) (Youssef et al., 2025), develops methods for text generation and summarization, (Trienes et al., 2023), (Koraş et al., 2024), (Nguyen et al., 2024) advances techniques for understanding LLM behavior (Trienes et al., 2025) (Trienes et al., 2024) and builds domain-specific NLP tools for healthcare, (Trienes et al., 2022), (Pathak et al., 2019), (Libbi et al., 2021).

If you want to know what mammoths have to do with natural language generation, check out this
ICLR Blog post on Plug-and-Play Language Models.

Applied Machine Learning

“Theory and practice sometimes clash. And when that happens, theory loses. Every single time.” — Linus Torvalds

We collaborate with partners in medicine, public administration, and industry to build machine learning systems that operate reliably under real-world constraints. Beyond accuracy, our models must be interpretable, fair, robust, and aligned with domain needs.

Our applied work includes developing decision-support models, (Pathak et al., 2025), (de Vries et al., 2021), creating AI systems that support users in practice, such as student-facing chatbots, (Trienes et al., 2025), and tools that assist clinical workflows, (Trienes et al., 2023),
as well as methods enabling legally compliant data sharing, (Libbi et al., 2021). We also investigate pressing challenges in applied ML, including shortcut learning and spurious correlations, (Le et al., 2025), (Nauta et al., 2022).

References

Jan Trienes, Anastasiia Derzhanskaia, Roland Schwarzkopf, Markus Mühling, Jörg Schlötterer, and Christin Seifert. Marcel: A Lightweight and Open-Source Conversational Agent for University Student Support. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 2025.

BibTeX

@inproceedings{Trienes2025_emnlp_marcel-chatbot,
  author = {Trienes, Jan and Derzhanskaia, Anastasiia and Schwarzkopf, Roland and M{\"u}hling, Markus and Schl{\"o}tterer, J{\"o}rg and Seifert, Christin},
  booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  title = {Marcel: A Lightweight and Open-Source Conversational Agent for University Student Support},
  year = {2025},
  address = {Suzhou, China},
  editor = {Habernal, Ivan and Schulam, Peter and Tiedemann, J{\"o}rg},
  month = nov,
  pages = {181--195},
  publisher = {Association for Computational Linguistics},
  code = {https://github.com/aix-group/marcel-chat},
  doi = {10.18653/v1/2025.emnlp-demos.13},
  isbn = {979-8-89176-334-0},
  url = {https://aclanthology.org/2025.emnlp-demos.13/}
}

Jan Trienes, Jörg Schlötterer, Junyi Jessy Li, and Christin Seifert. Behavioral Analysis of Information Salience in Large Language Models. Findings of the Association for Computational Linguistics: ACL 2025. 2025.

BibTeX

@inproceedings{Trienes2025_acl_information-salience,
  author = {Trienes, Jan and Schl{\"o}tterer, J{\"o}rg and Li, Junyi Jessy and Seifert, Christin},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2025},
  title = {Behavioral Analysis of Information Salience in Large Language Models},
  year = {2025},
  address = {Vienna, Austria},
  editor = {Che, Wanxiang and Nabende, Joyce and Shutova, Ekaterina and Pilehvar, Mohammad Taher},
  month = jul,
  pages = {23428--23454},
  publisher = {Association for Computational Linguistics},
  code = {https://github.com/jantrienes/llm-salience},
  doi = {10.18653/v1/2025.findings-acl.1204},
  isbn = {979-8-89176-256-5},
  url = {https://aclanthology.org/2025.findings-acl.1204/}
}

Phuong Quynh Le, Jörg Schlötterer, and Christin Seifert. Out of Spuriousity: Improving Robustness to Spurious Correlations without Group Annotations. Transactions on Machine Learning Research. 2025.

BibTeX

@article{Le2025_tmlr_spurious-correlations-wo-group-annotations,
  author = {Le, Phuong Quynh and Schl{\"o}tterer, J{\"o}rg and Seifert, Christin},
  journal = {Transactions on Machine Learning Research},
  title = {Out of Spuriousity: Improving Robustness to Spurious Correlations without Group Annotations},
  year = {2025},
  issn = {2835-8856},
  code = {https://github.com/aix-group/prusc},
  file = {:own-pdf/Le2025_tmlr_spurious-correlations-wo-group-annotations_publisher.pdf:PDF},
  url = {https://openreview.net/forum?id=EEeVYfXor5}
}

Paul Youssef, Zhixue Zhao, Daniel Braun, Jörg Schlötterer, and Christin Seifert. Position: Editing Large Language Models Poses Serious Safety Risks. Forty-second International Conference on Machine Learning Position Paper Track. 2025.

BibTeX

@inproceedings{Youssef2025_icml_position-llm-editing-safetey-risk,
  author = {Youssef, Paul and Zhao, Zhixue and Braun, Daniel and Schl{\"o}tterer, J{\"o}rg and Seifert, Christin},
  booktitle = {Forty-second International Conference on Machine Learning Position Paper Track},
  title = {Position: Editing Large Language Models Poses Serious Safety Risks},
  year = {2025},
  url = {https://openreview.net/forum?id=QLKBm1PaCU}
}

Shreyasi Pathak, Jörg Schlötterer, Jeroen Geerdink, Jeroen Veltman, Maurice van Keulen, Nicola Strisciuglio, and Christin Seifert. Breast cancer prediction using mammography exams for real hospital settings. Computers in Biology and Medicine. 2025.

BibTeX

@article{Pathatk2025_cbmj_breast-cancer-mammography,
  author = {Pathak, Shreyasi and Schlötterer, Jörg and Geerdink, Jeroen and Veltman, Jeroen and {van Keulen}, Maurice and Strisciuglio, Nicola and Seifert, Christin},
  journal = {Computers in Biology and Medicine},
  title = {Breast cancer prediction using mammography exams for real hospital settings},
  year = {2025},
  issn = {0010-4825},
  pages = {111136},
  volume = {198},
  code = {https://github.com/ShreyasiPathak/multiinstance-learning-mammography},
  doi = {https://doi.org/10.1016/j.compbiomed.2025.111136},
  file = {:own-pdf/Pathatk2025_cbmj_breast-cancer-mammography-realistic-settings_preprint.pdf:PDF},
  keywords = {Deep learning, Mammography images, Weakly supervised learning, Breast cancer prediction in real hospital settings},
  url = {https://www.sciencedirect.com/science/article/pii/S0010482525014891}
}

Van Bach Nguyen, Paul Youssef, Christin Seifert, and Jörg Schlötterer. LLMs for Generating and Evaluating Counterfactuals: A Comprehensive Study. Findings of the Association for Computational Linguistics: EMNLP 2024. 2024.

BibTeX

@inproceedings{Nguyen2024_emnlp_llms-for-generating-counterfactuals,
  author = {Nguyen, Van Bach and Youssef, Paul and Seifert, Christin and Schl{\"o}tterer, J{\"o}rg},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2024},
  title = {{LLM}s for Generating and Evaluating Counterfactuals: A Comprehensive Study},
  year = {2024},
  address = {Miami, Florida, USA},
  editor = {Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung},
  month = nov,
  pages = {14809--14824},
  publisher = {Association for Computational Linguistics},
  code = {https://github.com/aix-group/llms-for-cfs/},
  file = {:own-pdf/Nguyen2024_emnlp_llms-for-generating-counterfactuals_publisher.pdf:PDF},
  url = {https://aclanthology.org/2024.findings-emnlp.870}
}

Jan Trienes, Sebastian Joseph, Jörg Schlötterer, Christin Seifert, Kyle Lo, Wei Xu, Byron Wallace, and Junyi Jessy Li. InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

BibTeX

@inproceedings{Trienes2024_acl_infolossqa,
  author = {Trienes, Jan and Joseph, Sebastian and Schl{\"o}tterer, J{\"o}rg and Seifert, Christin and Lo, Kyle and Xu, Wei and Wallace, Byron and Li, Junyi Jessy},
  booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  title = {{I}nfo{L}oss{QA}: Characterizing and Recovering Information Loss in Text Simplification},
  year = {2024},
  address = {Bangkok, Thailand},
  editor = {Ku, Lun-Wei and Martins, Andre and Srikumar, Vivek},
  month = aug,
  pages = {4263--4294},
  publisher = {Association for Computational Linguistics},
  code = {https://github.com/jantrienes/InfoLossQA},
  url = {https://aclanthology.org/2024.acl-long.234}
}

Osman Alperen Koraş, Jörg Schlötterer, and Christin Seifert. A Second Look on BASS – Boosting Abstractive Summarization with Unified Semantic Graphs. Advances in Information Retrieval. 2024.

BibTeX

@inproceedings{Koras2024_ecir_bass-replication,
  author = {Kora{\c{s}}, Osman Alperen and Schl{\"o}tterer, J{\"o}rg and Seifert, Christin},
  booktitle = {Advances in Information Retrieval},
  title = {A Second Look on BASS -- Boosting Abstractive Summarization with Unified Semantic Graphs},
  year = {2024},
  address = {Cham},
  editor = {Goharian, Nazli and Tonellotto, Nicola and He, Yulan and Lipani, Aldo and McDonald, Graham and Macdonald, Craig and Ounis, Iadh},
  pages = {99--114},
  publisher = {Springer Nature Switzerland},
  doi = {10.1007/978-3-031-56066-8_11},
  file = {:own-pdf/Koras2024_ecir_bass-replication_preprint.pdf:PDF},
  isbn = {978-3-031-56066-8},
  url = {https://arxiv.org/abs/2403.02930}
}

Paul Youssef, Osman Koraş, Meijie Li, Jörg Schlötterer, and Christin Seifert. Give Me the Facts! A Survey on Factual Knowledge Probing in Pre-trained Language Models. Findings of the Association for Computational Linguistics: EMNLP 2023. 2023.

BibTeX

@inproceedings{Youssef2023_emnlp_survey-probing,
  author = {Youssef, Paul and Kora{\c{s}}, Osman and Li, Meijie and Schl{\"o}tterer, J{\"o}rg and Seifert, Christin},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2023},
  title = {Give Me the Facts! A Survey on Factual Knowledge Probing in Pre-trained Language Models},
  year = {2023},
  address = {Singapore},
  editor = {Bouamor, Houda and Pino, Juan and Bali, Kalika},
  month = dec,
  pages = {15588--15605},
  publisher = {ACL},
  doi = {10.18653/v1/2023.findings-emnlp.1043},
  file = {:own-pdf/Youssef2023_emnlp_survey-probing_preprint.pdf:PDF},
  url = {https://aclanthology.org/2023.findings-emnlp.1043}
}

Phuong Quynh Le, Meike Nauta, Van Bach Nguyen, Shreyasi Pathak, Jörg Schlötterer, and Christin Seifert. Benchmarking eXplainable AI - A Survey on Available Toolkits and Open Challenges. Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23. 2023.

BibTeX

@inproceedings{Le2023_ijcai_benchmarking-xai,
  author = {Le, Phuong Quynh and Nauta, Meike and Nguyen, Van Bach and Pathak, Shreyasi and Schl\"{o}tterer, J\"{o}rg and Seifert, Christin},
  booktitle = {Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, {IJCAI-23}},
  title = {{Benchmarking eXplainable AI - A Survey on Available Toolkits and Open Challenges}},
  year = {2023},
  editor = {Elkind, Edith},
  month = aug,
  note = {Survey Track},
  pages = {6665--6673},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  doi = {10.24963/ijcai.2023/747},
  file = {:own-pdf/Le2023_ijcai_benchmarking-xai_author.pdf:PDF},
  url = {https://doi.org/10.24963/ijcai.2023/747}
}

Meike Nauta, Jan Trienes, Shreyasi Pathak, Elisa Nguyen, Michelle Peters, Yasmin Schmitt, Jörg Schlötterer, Maurice van Keulen, and Christin Seifert. From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI. ACM Comput. Surv.. 2023.

BibTeX

@article{Nauta2023_csur_evaluating-xai-survey,
  author = {Nauta, Meike and Trienes, Jan and Pathak, Shreyasi and Nguyen, Elisa and Peters, Michelle and Schmitt, Yasmin and Schl\"{o}tterer, J\"{o}rg and van Keulen, Maurice and Seifert, Christin},
  journal = {ACM Comput. Surv.},
  title = {{From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI}},
  year = {2023},
  issn = {0360-0300},
  month = feb,
  address = {New York, NY, USA},
  comment = {https://utwente-dmb.github.io/xai-papers/},
  doi = {10.1145/3583558},
  file = {:own-pdf/Nauta2023_csur_evaluating-xai-survey_preprint-incl-suppl.pdf:PDF},
  keywords = {explainability, explainable AI, explainable artificial intelligence, XAI, interpretable machine learning, interpretability, quantitative evaluation methods, evaluation},
  publisher = {Association for Computing Machinery}
}

Meike Nauta, Jörg Schlötterer, Maurice van Keulen, and Christin Seifert. PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2023.

BibTeX

@inproceedings{Nauta2023_cvpr_pipnet,
  author = {Nauta, Meike and Schl\"{o}tterer, J\"{o}rg and van Keulen, Maurice and Seifert, Christin},
  booktitle = {{IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}},
  title = {{PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification}},
  year = {2023},
  code = {https://github.com/M-Nauta/PIPNet},
  doi = {10.1109/CVPR52729.2023.00269},
  file = {:own-pdf/Nauta2023_cvpr_pipnet.pdf:PDF},
  url = {https://openaccess.thecvf.com/content/CVPR2023/html/Nauta_PIP-Net_Patch-Based_Intuitive_Prototypes_for_Interpretable_Image_Classification_CVPR_2023_paper.html}
}

Jan Trienes, Paul Youssef, Jörg Schlötterer, and Christin Seifert. Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis. Proceedings of the 16th International Natural Language Generation Conference (INLG). 2023.

BibTeX

@inproceedings{Trienes2023_inlg_guidance-radiology-report-summarization,
  author = {Trienes, Jan and Youssef, Paul and Schl{\"o}tterer, J{\"o}rg and Seifert, Christin},
  booktitle = {Proceedings of the 16th International Natural Language Generation Conference (INLG)},
  title = {Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis},
  year = {2023},
  addendum = {\textit{Best Evaluation Paper Award Nomination}},
  code = {https://github.com/jantrienes/inlg2023-radsum},
  doi = {10.18653/v1/2023.inlg-main.13},
  file = {:/Volumes/Data/data-work/Research/Literature/own-pdf/Trienes2023_inlg_guidance-radiology-report-summarization_author.pdf:PDF}
}

Van Bach Nguyen, Jörg Schlötterer, and Christin Seifert. From Black Boxes to Conversations: Incorporating XAI in a Conversational Agent. Proc. World Conference Explainable Artificial Intelligence (XAI). 2023.

BibTeX

@inproceedings{Nguyen2023_wcxai_xagent,
  author = {Nguyen, Van Bach and Schl{\"o}tterer, J{\"o}rg and Seifert, Christin},
  booktitle = {Proc. World Conference Explainable Artificial Intelligence (XAI)},
  title = {From Black Boxes to Conversations: Incorporating XAI in a Conversational Agent},
  year = {2023},
  address = {Cham},
  editor = {Longo, Luca},
  pages = {71--96},
  publisher = {Springer Nature Switzerland},
  series = {World Conference on XAI},
  code = {https://github.com/bach1292/XAGENT/},
  doi = {10.1007/978-3-031-44070-0_4},
  file = {:own-pdf/Nguyen2023_wcxai_xagent_preprint.pdf:PDF},
  url = {https://arxiv.org/abs/2209.02552}
}

Andrea Papenmeier, Dagmar Kern, Gwenn Englebienne, and Christin Seifert. It’s Complicated: The Relationship between User Trust, Model Accuracy and Explanations in AI. ACM Trans. Comput.-Hum. Interact.. 2022.

BibTeX

@article{Papenmeier2022_tochi_trust-accuracy-explanations,
  author = {Papenmeier, Andrea and Kern, Dagmar and Englebienne, Gwenn and Seifert, Christin},
  journal = {ACM Trans. Comput.-Hum. Interact.},
  title = {{It’s Complicated: The Relationship between User Trust, Model Accuracy and Explanations in AI}},
  year = {2022},
  issn = {1073-0516},
  month = mar,
  number = {4},
  volume = {29},
  address = {New York, NY, USA},
  articleno = {35},
  doi = {10.1145/3495013},
  file = {:own-pdf/Papenmeier2022_tochi_xai-trust-accuracy_preprint.pdf:PDF},
  issue_date = {August 2022},
  keywords = {minimum explanations, explanation fidelity, user trust, machine learning, Explainable AI},
  numpages = {33},
  publisher = {Association for Computing Machinery}
}

Meike Nauta, Ricky Walsh, Adam Dubowski, and Christin Seifert. Uncovering and Correcting Shortcut Learning in Machine Learning Models for Skin Cancer Diagnosis. Diagnostics. 2022.

BibTeX

@article{Nauta2022_diagnostics_short-cut-learning-skin-cancer,
  author = {Nauta, Meike and Walsh, Ricky and Dubowski, Adam and Seifert, Christin},
  journal = {Diagnostics},
  title = {Uncovering and Correcting Shortcut Learning in Machine Learning Models for Skin Cancer Diagnosis},
  year = {2022},
  issn = {2075-4418},
  number = {1},
  volume = {12},
  article-number = {40},
  code = {https://github.com/adubowski/shortcuts-skin-cancer},
  doi = {10.3390/diagnostics12010040},
  file = {:own-pdf/Nauta2022_diagnostics_Correcting-Shortcut-Learning-Skin-Cancer_author-version.pdf:PDF},
  url = {https://www.mdpi.com/2075-4418/12/1/40}
}

Andrea Papenmeier, Dagmar Kern, Daniel Hienert, Yvonne Kammerer, and Christin Seifert. How Accurate Does It Feel? – Human Perception of Different Types of Classification Mistakes. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. 2022.

BibTeX

@inproceedings{Papenmeier2022a_chi_perceived-accuracy,
  author = {Papenmeier, Andrea and Kern, Dagmar and Hienert, Daniel and Kammerer, Yvonne and Seifert, Christin},
  booktitle = {Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems},
  title = {{How Accurate Does It Feel? -- Human Perception of Different Types of Classification Mistakes}},
  year = {2022},
  address = {New York, NY, USA},
  publisher = {Association for Computing Machinery},
  series = {CHI '22},
  doi = {10.1145/3491102.3501915},
  file = {:own-pdf/Papenmeier2022a_chi_percevied-accuracy_preprint.pdf:PDF}
}

Jan Trienes, Jörg Schlötterer, Hans-Ulrich Schildhaus, and Christin Seifert. Patient-friendly Clinical Notes: Towards a new Text Simplification Dataset. Proc. EMNLP Workshop on Text Simplification, Accessibility, and Readability (TSAR). 2022.

BibTeX

@inproceedings{Trienes2022_tsar_patient-friendly-clinical-notes,
  author = {Trienes, Jan and Schl\"{o}tterer, J\"{o}rg and Schildhaus, Hans-Ulrich and Seifert, Christin},
  booktitle = {Proc. EMNLP Workshop on Text Simplification, Accessibility, and Readability (TSAR)},
  title = {Patient-friendly Clinical Notes: Towards a new Text Simplification Dataset},
  year = {2022},
  code = {https://github.com/jantrienes/simple-patho},
  file = {:own-pdf/Trienes2022_tsar_patient-friendly-clinical-notes_preprint.pdf:PDF},
  url = {https://aclanthology.org/2022.tsar-1.3/}
}

Bram C. S. de Vries, Johannes H. Hegeman, Wieke Nijmeijer, Jeroen Geerdink, Christin Seifert, and Karin G. M. Groothuis-Oudshoorn. Comparing three machine learning approaches to design a risk assessment tool for future fractures: predicting a subsequent major osteoporotic fracture in fracture patients with osteopenia and osteoporosis. Osteoporosis International. 2021.

BibTeX

@article{Vries2021_osteoporosis_risk-assessment-for-future-fractures,
  author = {{de Vries}, Bram C. S. and Hegeman, Johannes H. and Nijmeijer, Wieke and Geerdink, Jeroen and Seifert, Christin and Groothuis-Oudshoorn, Karin G. M.},
  journal = {Osteoporosis International},
  title = {Comparing three machine learning approaches to design a risk assessment tool for future fractures: predicting a subsequent major osteoporotic fracture in fracture patients with osteopenia and osteoporosis},
  year = {2021},
  issn = {1433-2965},
  month = jan,
  doi = {10.1007/s00198-020-05735-z},
  file = {:/Volumes/Data/data-work/Research/Literature/bib/own/Vries2021_OstInt_ComparingThreeMachineLearningA.pdf:PDF;:/Volumes/Data/data-work/Research/Literature/bib/own/Vries2021_ostint_predicting-subsequent-fracture_author-version.pdf:PDF}
}

Meike Nauta, Ron van Bree, and Christin Seifert. Neural Prototype Trees for Interpretable Fine-grained Image Recognition. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2021.

BibTeX

@inproceedings{Nauta2021_cvpr_prototree,
  author = {Nauta, Meike and van Bree, Ron and Seifert, Christin},
  booktitle = {{IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}},
  title = {Neural Prototype Trees for Interpretable Fine-grained Image Recognition},
  year = {2021},
  pages = {14933--14943},
  code = {https://github.com/M-Nauta/ProtoTree},
  doi = {10.1109/CVPR46437.2021.01469},
  file = {:own-pdf/Nauta2021_cvpr_ProtoTree_proceedings-version.pdf:PDF}
}

Claudia Alessandra Libbi, Jan Trienes, Dolf Trieschnigg, and Christin Seifert. Generating Synthetic Training Data for Supervised De-Identification of Electronic Health Records. Future Internet. 2021.

BibTeX

@article{Libbi2021_futint_synthetic-data-deidentification,
  author = {Libbi, Claudia Alessandra and Trienes, Jan and Trieschnigg, Dolf and Seifert, Christin},
  journal = {Future Internet},
  title = {Generating Synthetic Training Data for Supervised De-Identification of Electronic Health Records},
  year = {2021},
  issn = {1999-5903},
  number = {5},
  volume = {13},
  article-number = {136},
  code = {https://github.com/nedap/mdpi2021-textgen},
  doi = {10.3390/fi13050136},
  file = {:own-pdf/Libbi2021-SyntheticTrainingData_preprint.pdf:PDF},
  url = {https://www.mdpi.com/1999-5903/13/5/136}
}

Shreyasi Pathak, Jorit van Rossen, Onno Vijlbrief, Jeroen Geerdink, Christin Seifert, and Maurice van Keulen. Post-Structuring Radiology Reports of Breast Cancer Patients for Clinical Quality Assurance. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2019.

BibTeX

@article{Pathak2019_tcbb_post-structuring-radiology-reports,
  author = {Pathak, Shreyasi and {van Rossen}, Jorit and Vijlbrief, Onno and Geerdink, Jeroen and Seifert, Christin and {van Keulen}, Maurice},
  journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
  title = {Post-Structuring Radiology Reports of Breast Cancer Patients for Clinical Quality Assurance},
  year = {2019},
  issn = {2375-9259},
  month = may,
  code = {https://github.com/ShreyasiPathak/AutomaticStructuringBreastCancerReports},
  doi = {10.1109/TCBB.2019.2914678},
  file = {:own/Pathak2019_tcbb_post-structuring-radiology-reports_preprint.pdf:PDF},
  keywords = {Radiology;Breast cancer;Quality assurance;Standards;Task analysis;Machine learning;Natural language processing;Quality Assurance;Automatic Structuring;Post-Structuring;Radiology Reports;Conditional Random Field}
}

xAI Lab

Explainable AI

Language & LLMs

Applied ML

Explainable AI

Language & LLMs

Applied Machine Learning

References