2022

1.
Borchert, F., Schapranow, M.-P.: HPI-DHC @ BioASQ DisTEMIST: Spanish Biomedical Entity Linking with Pre-trained Transformers and Cross-lingual Candidate Retrieval. Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum. bll. 244–258. , Bologna, Italy (2022).
[ BibTeX ] [ URL ]
 
@inproceedings{borchert2022clef, address = {Bologna, Italy}, author = {Borchert, Florian and Schapranow, Matthieu-P.}, booktitle = {Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum}, keywords = {nlp myown sys:relevantfor:dhc highmed fb-boettinger}, month = {September}, pages = {244-258}, title = {HPI-DHC @ BioASQ DisTEMIST: Spanish Biomedical Entity Linking with Pre-trained Transformers and Cross-lingual Candidate Retrieval}, year = 2022 }

2.
Borchert, F., Lohr, C., Modersohn, L., Witt, J., Langer, T., Follmann, M., Gietzelt, M., Arnrich, B., Hahn, U., Schapranow, M.-P.: GGPONC 2.0 - The German Clinical Guideline Corpus for Oncology: Curation Workflow, Annotation Policy, Baseline NER Taggers. Proceedings of the Language Resources and Evaluation Conference. bll. 3650–3660. European Language Resources Association, Marseille, France (2022).
[ Abstract ] [ BibTeX ] [ URL ]
 
Despite remarkable advances in the development of language resources over the recent years, there is still a shortage of annotated, publicly available corpora covering (German) medical language. With the initial release of the German Guideline Program in Oncology NLP Corpus (GGPONC), we have demonstrated how such corpora can be built upon clinical guidelines, a widely available resource in many natural languages with a reasonable coverage of medical terminology. In this work, we describe a major new release for GGPONC. The corpus has been substantially extended in size and re-annotated with a new annotation scheme based on SNOMED CT top level hierarchies, reaching high inter-annotator agreement (γ=.94). Moreover, we annotated elliptical coordinated noun phrases and their resolutions, a common language phenomenon in (not only German) scientific documents. We also trained BERT-based named entity recognition models on this new data set, which achieve high performance on short, coarse-grained entity spans (F1=.89), while the rate of boundary errors increases for long entity spans. GGPONC is freely available through a data use agreement. The trained named entity recognition models, as well as the detailed annotation guide, are also made publicly available.
@inproceedings{borchert-EtAl:2022:LREC, abstract = {Despite remarkable advances in the development of language resources over the recent years, there is still a shortage of annotated, publicly available corpora covering (German) medical language. With the initial release of the German Guideline Program in Oncology NLP Corpus (GGPONC), we have demonstrated how such corpora can be built upon clinical guidelines, a widely available resource in many natural languages with a reasonable coverage of medical terminology. In this work, we describe a major new release for GGPONC. The corpus has been substantially extended in size and re-annotated with a new annotation scheme based on SNOMED CT top level hierarchies, reaching high inter-annotator agreement (γ=.94). Moreover, we annotated elliptical coordinated noun phrases and their resolutions, a common language phenomenon in (not only German) scientific documents. We also trained BERT-based named entity recognition models on this new data set, which achieve high performance on short, coarse-grained entity spans (F1=.89), while the rate of boundary errors increases for long entity spans. GGPONC is freely available through a data use agreement. The trained named entity recognition models, as well as the detailed annotation guide, are also made publicly available.}, address = {Marseille, France}, author = {Borchert, Florian and Lohr, Christina and Modersohn, Luise and Witt, Jonas and Langer, Thomas and Follmann, Markus and Gietzelt, Matthias and Arnrich, Bert and Hahn, Udo and Schapranow, Matthieu-P.}, booktitle = {Proceedings of the Language Resources and Evaluation Conference}, keywords = {nlp sys:relevantfor:dhc highmed fb-boettinger}, month = {June}, pages = {3650–3660}, publisher = {European Language Resources Association}, title = {GGPONC 2.0 - The German Clinical Guideline Corpus for Oncology: Curation Workflow, Annotation Policy, Baseline NER Taggers}, year = 2022 }

3.
Henkenjohann, R., Bergner, B., Borchert, F., Bougatf, N., Hund, H., Eils, R., Schapranow, M.-P.: An Engineering Approach towards Multi-Site Virtual Molecular Tumor Board Software Support. In: Pissaloux, E., Papadopoulos, G., Achilleos, A., en Velázquez, R. (reds.) ICT for Health, Accessibility and Wellbeing. IHAW 2021. bll. 156–170. Springer, Cham (2022).
[ BibTeX ] [ URL ]
 
@inproceedings{henkenjohann2021mtb, author = {Henkenjohann, Richard and Bergner, Benjamin and Borchert, Florian and Bougatf, Nina and Hund, Hauke and Eils, Roland and Schapranow, Matthieu-P.}, booktitle = {ICT for Health, Accessibility and Wellbeing. IHAW 2021}, editor = {Pissaloux, E. and Papadopoulos, G.A. and Achilleos, A. and Velázquez, R.}, keywords = {myown highmed mtb}, month = {mar}, pages = {156-170}, publisher = {Springer, Cham.}, series = {Communications in Computer and Information Science}, title = {An Engineering Approach towards Multi-Site Virtual Molecular Tumor Board Software Support}, volume = 1538, year = 2022 }

2021

1.
Rasheed, A., Borchert, F., Kohlmeyer, L., Henkenjohann, R., Schapranow, M.-P.: A Comparison of Concept Embeddings for German Clinical Corpora. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). bll. 2314–2321 (2021).
[ Abstract ] [ BibTeX ] [ URL ]
 
Clinical concept embeddings enable unsupervised learning of relationships among medical concepts. A range of benchmarks quantifies the degree to which learned representations capture medical semantics. However, training and evaluation of embeddings require a large amount of data. In addition, embeddings’ benchmark score varies in different languages because it differs with the size of the available corpora. Multi-modal data increases the corpus size, but data protection regulations limit access to clinical multi-modal data. We present an extendable pipeline for training clinical concept embeddings on various text corpora and evaluating the quality of trained embeddings on selected benchmark tasks. Our work provides different ways to identify clinical concepts in textual corpora. We train embeddings on selected German clinical text corpora and evaluate them on various benchmark scores. Our work can be extended to train embeddings in other languages in which a large multi-modal dataset is not available.
@inproceedings{rasheed_concept_embeddings, abstract = {Clinical concept embeddings enable unsupervised learning of relationships among medical concepts. A range of benchmarks quantifies the degree to which learned representations capture medical semantics. However, training and evaluation of embeddings require a large amount of data. In addition, embeddings’ benchmark score varies in different languages because it differs with the size of the available corpora. Multi-modal data increases the corpus size, but data protection regulations limit access to clinical multi-modal data. We present an extendable pipeline for training clinical concept embeddings on various text corpora and evaluating the quality of trained embeddings on selected benchmark tasks. Our work provides different ways to identify clinical concepts in textual corpora. We train embeddings on selected German clinical text corpora and evaluate them on various benchmark scores. Our work can be extended to train embeddings in other languages in which a large multi-modal dataset is not available.}, author = {Rasheed, Aadil and Borchert, Florian and Kohlmeyer, Lasse and Henkenjohann, Richard and Schapranow, Matthieu-P.}, booktitle = {2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)}, keywords = {nlp myown sys:relevantfor:dhc highmed fb-boettinger}, pages = {2314-2321}, title = {A Comparison of Concept Embeddings for German Clinical Corpora}, year = 2021 }

2.
Borchert, F., Meister, L., Langer, T., Follmann, M., Arnrich, B., Schapranow, M.-P.: Controversial Trials First: Identifying Disagreement Between Clinical Guidelines and New Evidence. AMIA Annual Symposium Proceedings. bll. 237–246. American Medical Informatics Association (2021).
[ BibTeX ] [ URL ] [ Download ]
 
@inproceedings{borchert_amia2021, author = {Borchert, Florian and Meister, Laura and Langer, Thomas and Follmann, Markus and Arnrich, Bert and Schapranow, Matthieu-P.}, booktitle = {AMIA Annual Symposium Proceedings}, keywords = {nlp myown sys:relevantfor:dhc highmed fb-boettinger}, organization = {American Medical Informatics Association}, pages = {237-246}, title = {Controversial Trials First: Identifying Disagreement Between Clinical Guidelines and New Evidence}, volume = 2021, year = 2021 }

3.
Borchert, F., Mock, A., Tomczak, A., Hügel, J., Alkarkoukly, S., Knurr, A., Volckmar, A.-L., Stenzinger, A., Schirmacher, P., Debus, J., Jäger, D., Longerich, T., Fröhling, S., Eils, R., Bougatf, N., Sax, U., Schapranow, M.-P.: Knowledge bases and software support for variant interpretation in precision oncology. Briefings in Bioinformatics. 22, (2021).
[ Abstract ] [ BibTeX ] [ URL ]
 
Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process.
@article{10.1093/bib/bbab134, abstract = {Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process.}, author = {Borchert, Florian and Mock, Andreas and Tomczak, Aurelie and Hügel, Jonas and Alkarkoukly, Samer and Knurr, Alexander and Volckmar, Anna-Lena and Stenzinger, Albrecht and Schirmacher, Peter and Debus, Jürgen and Jäger, Dirk and Longerich, Thomas and Fröhling, Stefan and Eils, Roland and Bougatf, Nina and Sax, Ulrich and Schapranow, Matthieu-P}, journal = {Briefings in Bioinformatics}, keywords = {HiGHmed myown sys:relevantfor:dhc fb-boettinger}, month = {05}, note = {bbab134}, number = 6, title = {Knowledge bases and software support for variant interpretation in precision oncology}, volume = 22, year = 2021 }

2020

1.
Borchert, F., Lohr, C., Modersohn, L., Hahn, U., Langer, T., Wenzel, G., Follmann, M., Schapranow, M.-P.: "Herr Doktor, verstehen Sie mich?“: Wie lernende Systeme helfen medizinische Fachsprache zu verstehen und welche Rolle klinische Leitlinien dabei spielen. gesundhyte.de: Das Magazin für Digitale Gesundheit in Deutschland. 13, 19–22 (2020).
[ BibTeX ]
 
@article{borchert2020doktor, author = {Borchert, Florian and Lohr, Christina and Modersohn, Luise and Hahn, Udo and Langer, Thomas and Wenzel, Gregor and Follmann, Markus and Schapranow, Matthieu-P.}, journal = {gesundhyte.de: Das Magazin für Digitale Gesundheit in Deutschland}, keywords = {HiGHmed myown}, month = {dec}, pages = {19–22}, title = {"Herr Doktor, verstehen Sie mich?“: Wie lernende Systeme helfen medizinische Fachsprache zu verstehen und welche Rolle klinische Leitlinien dabei spielen}, volume = 13, year = 2020 }

2.
Borchert, F., Lohr, C., Modersohn, L., Langer, T., Follmann, M., Sachs, J.P., Hahn, U., Schapranow, M.-P.: GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines. Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis. bll. 38–48. Association for Computational Linguistics, Online (2020).
[ Abstract ] [ BibTeX ] [ URL ]
 
The lack of publicly accessible text corpora is a major obstacle for progress in natural language processing. For medical applications, unfortunately, all language communities other than English are low-resourced. In this work, we present GGPONC (German Guideline Program in Oncology NLP Corpus), a freely distributable German language corpus based on clinical practice guidelines for oncology. This corpus is one of the largest ever built from German medical documents. Unlike clinical documents, clinical guidelines do not contain any patient-related information and can therefore be used without data protection restrictions. Moreover, GGPONC is the first corpus for the German language covering diverse conditions in a large medical subfield and provides a variety of metadata, such as literature references and evidence levels. By applying and evaluating existing medical information extraction pipelines for German text, we are able to draw comparisons for the use of medical language to other corpora, medical and non-medical ones.
@inproceedings{borchert-etal-2020-ggponc, abstract = {The lack of publicly accessible text corpora is a major obstacle for progress in natural language processing. For medical applications, unfortunately, all language communities other than English are low-resourced. In this work, we present GGPONC (German Guideline Program in Oncology NLP Corpus), a freely distributable German language corpus based on clinical practice guidelines for oncology. This corpus is one of the largest ever built from German medical documents. Unlike clinical documents, clinical guidelines do not contain any patient-related information and can therefore be used without data protection restrictions. Moreover, GGPONC is the first corpus for the German language covering diverse conditions in a large medical subfield and provides a variety of metadata, such as literature references and evidence levels. By applying and evaluating existing medical information extraction pipelines for German text, we are able to draw comparisons for the use of medical language to other corpora, medical and non-medical ones.}, address = {Online}, author = {Borchert, Florian and Lohr, Christina and Modersohn, Luise and Langer, Thomas and Follmann, Markus and Sachs, Jan Philipp and Hahn, Udo and Schapranow, Matthieu-P.}, booktitle = {Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis}, keywords = {nlp HiGHmed myown sys:relevantfor:dhc fb-boettinger}, month = {nov}, pages = {38–48}, publisher = {Association for Computational Linguistics}, title = {{GGPONC}: A Corpus of {G}erman Medical Text with Rich Metadata Based on Clinical Practice Guidelines}, year = 2020 }