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Deep Learning Approaches Outperform Conventional Strategies in De-Identification of German Medical Reports

https://doi.org/10.3233/shti190813

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Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Clinical information extraction for lower-resource languages and domains with few-shot learning using pretrained language models and prompting

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Comparative analysis of machine learning algorithms for computer-assisted reporting based on fully automated cross-lingual RadLex mappings

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A Transformer-Based Pipeline for German Clinical Document De-Identification

inferred← citeshttps://doi.org/10.1109/bibm52615.2021.9669851openalex-snapshot

A Comparison of Concept Embeddings for German Clinical Corpora

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A Short Survey of LSTM Models for De-identification of Medical Free Text

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Automatic extraction of 12 cardiovascular concepts from German discharge letters using pre-trained language models

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Dynamic Privacy-Preserving Recommendations on Academic Graph Data

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Masketeer: An Ensemble-Based Pseudonymization Tool with Entity Recognition for German Unstructured Medical Free Text

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Large Language Models for Electronic Health Record De-Identification in English and German

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