| Large language models are zero-shot clinical information extractors M Agrawal, S Hegselmann, H Lang, Y Kim, D Sontag arXiv preprint arXiv:2205.12689, 2022 | 21 | 2022 |
| CDEGenerator: an online platform to learn from existing data models to build model registries J Varghese, M Fujarski, S Hegselmann, P Neuhaus, M Dugas Clinical epidemiology, 961-970, 2018 | 18 | 2018 |
| Reproducible survival prediction with SEER cancer data S Hegselmann, L Gruelich, J Varghese, M Dugas Machine Learning for Healthcare Conference, 49-66, 2018 | 14 | 2018 |
| Large language models are few-shot clinical information extractors M Agrawal, S Hegselmann, H Lang, Y Kim, D Sontag Proceedings of the 2022 Conference on Empirical Methods in Natural Language …, 2022 | 11 | 2022 |
| Counting in team semantics E Grädel, S Hegselmann 25th EACSL Annual Conference on Computer Science Logic (CSL 2016), 2016 | 11 | 2016 |
| An Evaluation of the Doctor-Interpretability of Generalized Additive Models with Interactions S Hegselmann, T Volkert, H Ohlenburg, A Gottschalk, M Dugas, C Ertmer Machine Learning for Healthcare Conference, 46-79, 2020 | 10 | 2020 |
| TabLLM: few-shot classification of tabular data with large language models S Hegselmann, A Buendia, H Lang, M Agrawal, X Jiang, D Sontag International Conference on Artificial Intelligence and Statistics, 5549-5581, 2023 | 7 | 2023 |
| A Web Service to Suggest Semantic Codes Based on the MDM-Portal. S Hegselmann, M Storck, S Geßner, P Neuhaus, J Varghese, M Dugas GMDS, 35-39, 2018 | 6 | 2018 |
| Automatic Conversion of Metadata from the Study of Health in Pomerania to ODM. S Hegselmann, S Gessner, P Neuhaus, J Henke, CO Schmidt, M Dugas eHealth, 88-96, 2017 | 5 | 2017 |
| Pragmatic MDR: a metadata repository with bottom-up standardization of medical metadata through reuse S Hegselmann, M Storck, S Gessner, P Neuhaus, J Varghese, P Bruland, ... BMC medical informatics and decision making 21 (1), 1-14, 2021 | 4 | 2021 |
| Compatible Data Models at Design Stage of Medical Information Systems: Leveraging Related Data Elements from the MDM Portal. M Dugas, S Hegselmann, S Riepenhausen, P Neuhaus, L Greulich, ... MedInfo, 113-117, 2019 | 4 | 2019 |
| Automated Transformation of CDISC ODM to OpenClinica. S Gessner, M Storck, S Hegselmann, M Dugas, IS Rey GMDS, 95-99, 2017 | 4 | 2017 |
| Inverted HMM-a Proof of Concept P Doetsch, S Hegselmann, R Schlüter, H Ney Neural Information Processing Systems Workshop, Barcelona, Spain, 2016 | 4 | 2016 |
| Development and validation of an interpretable 3-day intensive care unit readmission prediction model using explainable boosting machines S Hegselmann, C Ertmer, T Volkert, A Gottschalk, M Dugas, J Varghese medRxiv, 2021.11. 01.21265700, 2021 | 2 | 2021 |
| Semantically Annotated Metadata: Interconnecting Samply. MDR and MDM-Portal. A Vengadeswaran, P Neuhaus, S Hegselmann, H Storf, D Kadioglu GMDS, 86-92, 2019 | 2 | 2019 |
| Portal of Medical Data Models: Status 2018. S Riepenhausen, J Varghese, P Neuhaus, M Storck, A Meidt, ... EFMI-STC, 239-240, 2019 | 2 | 2019 |
| Standardising the development of ODM converters: the ODMToolBox I Soto-Rey, P Neuhaus, P Bruland, S Geßner, J Varghese, S Hegselmann, ... Stud Health Technol Inform 247, 231-235, 2018 | 2 | 2018 |
| An Open-Source, Standard-Compliant, and Mobile Electronic Data Capture System for Medical Research (OpenEDC): Design and Evaluation Study L Greulich, S Hegselmann, M Dugas JMIR Medical Informatics 9 (11), e29176, 2021 | 1 | 2021 |
| Recent Advances, Applications and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2022 Symposium S Hegselmann, H Zhou, Y Zhou, J Chien, S Nagaraj, N Hulkund, S Bhave, ... https://doi.org/10.5281/zenodo.7951122, 2023 | | 2023 |
| Machine Learning for Health (ML4H) 2022 A Parziale, M Agrawal, S Tang, K Severson, L Oala, A Subbaswamy, ... Machine Learning for Health, 1-11, 2022 | | 2022 |
