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A Transformer-Based Model Trained on Large Scale Claims Data for Prediction of Severe COVID-19 Disease Progression.

M. Lentzen, T. Linden, S. Veeranki, S. Madan, D. Kramer, W. Leodolter, and H. Fröhlich. IEEE J. Biomed. Health Informatics, 27 (9): 4548-4558 (September 2023)

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Holger Fröhlich

Stefan Fröhlich

Modellbasierte Fehlererkennung und Diagnose in Echtzeit am Beispiel einer BrennstoffzelleS. Fröhlich. Uni Kassel, (2009)

Other publications of authors with the same name

Optimal assignment kernels for attributed molecular graphsH. Fröhlich, J. Wegner, F. Sieker, and A. Zell. Proceedings of the 22nd International Conference on Machine Learning (ICML 2005), page 225--232. Bonn, Germany, ACM Press, (August 2005)An Explainable Multimodal Neural Network Architecture for Predicting Epilepsy Comorbidities Based on Administrative Claims Data.T. Linden, J. de Jong, C. Lu, V. Kiri, K. Haeffs, and H. Fröhlich. Frontiers Artif. Intell., (2021)Deep Learning-based detection of psychiatric attributes from German mental health records.S. Madan, F. Zimmer, H. Balabin, S. Schaaf, H. Fröhlich, J. Fluck, I. Neuner, K. Mathiak, M. Hofmann-Apitius, and P. Sarkheil. Int. J. Medical Informatics, (2022)PathME: pathway based multi-modal sparse autoencoders for clustering of patient-level multi-omics data.A. Lemsara, S. Ouadfel, and H. Fröhlich. BMC Bioinform., 21 (1): 146 (2020)Variational Autoencoder Modular Bayesian Networks for Simulation of Heterogeneous Clinical Study Data.L. Gootjes-Dreesbach, M. Sood, A. Sahay, M. Hofmann-Apitius, and H. Fröhlich. Frontiers Big Data, (2020)SEEDS: data driven inference of structural model errors and unknown inputs for dynamic systems biology.T. Newmiwaka, B. Engelhardt, P. Wendland, D. Kahl, H. Fröhlich, and M. Kschischo. Bioinform., 37 (9): 1330-1331 (2021)Towards early diagnosis of Alzheimer's disease: Advances in immune-related blood biomarkers and computational modeling approaches.S. Krix, E. Wilczynski, N. Falgàs, R. Sánchez-Valle, E. Yoles, U. Nevo, K. Baruch, and H. Fröhlich. CoRR, (2023)Synthetic data generation for a longitudinal cohort study - Evaluation, method extension and reproduction of published data analysis results.L. Kühnel, J. Schneider, I. Perrar, T. Adams, F. Prasser, U. Nöthlings, H. Fröhlich, and J. Fluck. CoRR, (2023)Ten quick tips for biomarker discovery and validation analyses using machine learning.R. Díaz-Uriarte, E. de Lope, R. Giugno, H. Fröhlich, P. Nazarov, I. Nepomuceno-Chamorro, A. Rauschenberger, and E. Glaab. PLoS Comput. Biol., (2022)A Transformer-Based Model Trained on Large Scale Claims Data for Prediction of Severe COVID-19 Disease Progression.M. Lentzen, T. Linden, S. Veeranki, S. Madan, D. Kramer, W. Leodolter, and H. Fröhlich. IEEE J. Biomed. Health Informatics, 27 (9): 4548-4558 (September 2023)

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Disambiguation of "Fröhlich, Holger"

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A Transformer-Based Model Trained on Large Scale Claims Data for Prediction of Severe COVID-19 Disease Progression.

Please choose a person to relate this publication to

Holger Fröhlich

Holger Fröhlich

Holger Fröhlich

Holger Fröhlich

Stefan Fröhlich

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Fröhlich, Holger"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML A Transformer-Based Model Trained on Large Scale Claims Data for Prediction of Severe COVID-19 Disease Progression.

Please choose a person to relate this publication to

Holger Fröhlich

Holger Fröhlich

Holger Fröhlich

Holger Fröhlich

Stefan Fröhlich

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
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MSOffice XML

A Transformer-Based Model Trained on Large Scale Claims Data for Prediction of Severe COVID-19 Disease Progression.