Category Archives: Projects

GeMTeX: German Medical Text Corpus

Executive Summary

In everyday clinical practice, there are many texts such as doctor’s letters and findings that contain valuable information about the patient’s medical history, progression and treatment. With the help of these texts, programs for the automatic processing of natural language (natural language processing, NLP for short) could support doctors and researchers in their work. However, the full potential of clinical documents cannot be exploited due to a lack of standardization. The German Medical Text Corpus (GeMTeX) method platform aims to close this gap and aims to make medical texts from patient care available for research projects available. The aim is to create the largest medical text corpus in the German language. You might want to refer to the GeMTeX consoritum website for further details.

Project Partners

Publications

    Research Publications

    • Fox, S., Preiß, M., Borchert, F., Rasheed, A., Schapranow, M.-P.: HPIDHC at NTCIR-17 MedNLP-SC: Data Augmentation and Ensemble Learning for Multilingual Adverse Drug Event Detection. NTCIR 17 Conference: Proceedings of the 17th NTCIR Conference on Evaluation of Information Access Technologies. bll. 185–192. , Tokyo, Japan (2023).
    • Borchert, F., Llorca, I., Schapranow, M.-P.: HPI-DHC @ BC8 SympTEMIST Track: Detection and Normalization of Symptom Mentions with SpanMarker and xMEN. In: Islamaj, R., Arighi, C., Campbell, I., Gonzalez-Hernandez, G., Hirschman, L., Krallinger, M., Lima-López, S., Weissenbacher, D., en Lu, Z. (reds.) Proceedings of the BioCreative VIII Challenge and Workshop: Curation and Evaluation in the era of Generative Models. , New Orleans, LA (2023).
    • Borchert, F., Llorca, I., Roller, R., Arnrich, B., Schapranow, M.-P.: xMEN: A Modular Toolkit for Cross-Lingual Medical Entity Normalization. arXiv preprint arXiv:2310.11275. (2023).
    • Borchert, F., Llorca, I., Schapranow, M.-P.: Cross-Lingual Candidate Retrieval and Re-ranking for Biomedical Entity Linking. In: Arampatzis, A., Kanoulas, E., Tsikrika, T., Vrochidis, S., Giachanou, A., Li, D., Aliannejadi, M., Vlachos, M., Faggioli, G., en Ferro, N. (reds.) Experimental IR Meets Multilinguality, Multimodality, and Interaction. bll. 135–147. Springer Nature Switzerland, Cham (2023).
    • Llorca, I., Borchert, F., Schapranow, M.-P.: A Meta-dataset of German Medical Corpora: Harmonization of Annotations and Cross-corpus NER Evaluation. Proceedings of the 5th Clinical Natural Language Processing Workshop. bll. 171–181. Association for Computational Linguistics, Toronto, Canada (2023).
    • Kämmer, N., and Borchert, F., and Winkler, S., and de Melo, G., and Schapranow, M.-P.: Resolving Elliptical Compounds in German Medical Text. The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks. bll. 292–305. Association for Computational Linguistics, Toronto, Canada (2023).

    Events

    Sponsors

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    NutriAct Logo

    NutriAct Competence Cluster

    Executive Summary

    Being fit and healthy even at high age is a high value for many people. A key factor in this is a healthy diet. People, who eat well and healthily throughout their lives, are less likely to be ill in old age. But how exactly is healthy nutrition defined? How healthy do older people in Germany eat? Would they even accept new nutrition recommendations? The competence cluster Nutritional Intervention for Healthy Aging (NutriAct) is investigating these questions in the Berlin-Potsdam area. The aim is to create the scientific basis for age-appropriate nutritional interventions and recommendations. In addition, new foods are to be developed that enable healthy nutrition. The cluster focuses in particular on the middle-aged population (50-70 years) in order to promote healthy aging beyond the age of 70. To this end, the neurobiological, psychological, social and familial basis of food choices and, in particular, their influencing factors within family structures, will be analyzed. In addition, research will be conducted to determine whether sociocultural factors influence taste preferences and dietary practices. A central question here is the significance of gender-specific effects in connection with taste orientations within a partnership. In addition, the extent to which moments of upheaval, such as the transition to retirement, separation, or an illness, influence eating practices will be examined. On the basis of the nutrition pattern analyses, realistically implementable nutrition recommendations will be developed. The central element of the cluster is an intervention study in which the effectiveness, adherence and acceptance of the NutriAct dietary pattern rich in plant proteins, fiber and unsaturated fatty acids will be systematically tested. Based on this, new healthy and palatable foods will be developed that will be accepted by consumers. Accompanying this will be the identification of new biomarkers that predict the effects of a particular diet on health status, especially in old age.

    You might want to refer to the NutriAct consoritum website for further details.

    Project Partners

    • German Institute of Human Nutrition
    • University of Potsdam
    • Technical University Berlin, Department of Food Technology and Food Material Science
    • Charité – Universitätsmedizin Berlin, Department for Endocrinology, Diabetes and Nutritional Medicine
    • Leibniz Institute of Vegetable and Ornamental Crops (IGZ)
    • Leibniz Institute of Agricultural Engineering and Bio-economy (ATB)
    • Institute for Food and Environmental Research (ILU)
    • Hasso Plattner Institute and AnalyzeGenomes.com

    Publications

      Research Publications

        Events

        Sponsors

        BMBF Logo

        NephroCAGE Logo

        NephroCAGE: German-Canadian Consortium on AI for Improved Kidney Transplantation Outcome

        Executive Summary

        The German-Canadian consortium NephroCAGE is cooperating to demonstrate the added value of artificial intelligence (AI) using the concrete clinical example of kidney transplantation. Inadequate kidney function requires regular dialysis: there are currently around 100,000 dialysis patients in Germany, and around half that number in Canada. Dialysis costs approximately 30-40k EUR per patient per year. In comparison, a kidney transplant costs around 15-20 thousand euros. In 2019, more than 2,100 kidney transplants were performed in Germany (German Organ Transplant Foundation) and more than 1,700 in Canada (Canadian Institute for Health in Canada). However, suitable donor organs are rare: in Germany, for example, there are more than 7,000 patients on a waiting list, and in Canada more than 3,000. Even after a transplant, there is a risk of complications that can lead to severe limitations in kidney function or, in the worst case, even total loss of the organ.

        The consortium partners are creating a learning AI system to match organ donors and recipients even more precisely in advance (matching) and thus prevent risks in kidney transplants. To this end, clinical centers of excellence in both nations are contributing transplant data from the last ten years. They will be analyzed using AI learning techniques and combined together with a novel matching algorithm to create clinical prognostic models for kidney transplant patients. By using a federated learning approach, where the algorithms are executed at the location of the data, data protection is maintained and sensitive health data from both nations can serve as a common basis for clinical prognostic models for the first time. As a result, a clinical demonstrator will be created to serve the exploitation of the medical and technical innovations in the context of care, as well as a basis for exploitation and follow-on projects.

        Project Partners

        Canada

        • The University of British Columbia, Faculty of Medicine, Department of Pathology and Laboratory Medicine, Vancouver, British Columbia
        • Genome BC, Vancouver, British Columbia 
        • Genome Canada, Ontario, Ottawa 
        • McGill University Health Centre, Montréal, Québec
        • Centre hospitalier de l’Université de Montréal, Québec
        • Genome Quebec, Montréal, Québec

        Germany

         

        Publications

        Research Publications

        • Schapranow, M.-P.: NephroCAGE: Wie Künstliche Intelligenz bei Nierenversagen unterstützen kann. Gesundhyte: Forschung neu vernetzen. 15, 100—102 (2023).
        • Schapranow, M.-P., Bayat, M., Rasheed, A., Naik, M., Graf, V., Schmidt, D., Budde, K., Cardinal, H., Sapir-Pichhadze, R., Fenninger, F., Sherwood, K., Keown, P., Günther, O., Pandl, K., Leiser, F., Thiebes, S., Sunyaev, A., Niemann, M., Schimanski, A., Klein, T.: NephroCAGE—German-Canadian Consortium on AI for Improved Kidney Transplantation Outcome: Protocol for an Algorithm Development and Validation Study. JMIR Res Protoc 2023. 12, (2023).
        • Borchert, F., Llorca, I., Schapranow, M.-P.: Cross-Lingual Candidate Retrieval and Re-ranking for Biomedical Entity Linking. In: Arampatzis, A., Kanoulas, E., Tsikrika, T., Vrochidis, S., Giachanou, A., Li, D., Aliannejadi, M., Vlachos, M., Faggioli, G., en Ferro, N. (reds.) Experimental IR Meets Multilinguality, Multimodality, and Interaction. bll. 135–147. Springer Nature Switzerland, Cham (2023).
        • Llorca, I., Borchert, F., Schapranow, M.-P.: A Meta-dataset of German Medical Corpora: Harmonization of Annotations and Cross-corpus NER Evaluation. Proceedings of the 5th Clinical Natural Language Processing Workshop. bll. 171–181. Association for Computational Linguistics, Toronto, Canada (2023).
        • Kämmer, N., and Borchert, F., and Winkler, S., and de Melo, G., and Schapranow, M.-P.: Resolving Elliptical Compounds in German Medical Text. The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks. bll. 292–305. Association for Computational Linguistics, Toronto, Canada (2023).

        Events

        Sponsors

        The project is generously sponsored by the German Federal Ministry for Economic Affairs and Climate Action (2021-2022) as project of strategic interest.

        Global Availability of 2019-nCoV Coronavirus Data Analysis Tool

        Today, we are happy to announce the general availability of the 2019-nCoV Coronavirus Data Analysis Tool by HPI. After days of closed beta phase, we just finished integrating the valuable user feedback. Finally, the 2019-nCoV Coronavirus Data Analysis Tool is available for public use. Furthermore, we decided that improvements and functional extensions from now on will be directly integrated in the productive version. Thus, users can benefit much faster from them. Currently, we are working on adding additional situation reports to provide valuable insights on the latest development of the pandemia.

        PS.: If you think that the tool is helpful, feel free to share the link. If you miss specific data or functionality, please feel free to send us your feedback to extend the functionality.

        Molecular Tumor Board

        Executive Summary

        Today, treatment alternatives for every oncology patient are individually discussed by medical

        experts in so-called tumor boards. However, the molecular analysis of each tumors is very complex and the assessment of individual tumor variants time-consuming. Therefore, specialized molecular tumor boards focus also on the analysis and therapy assessment incorporating fine-grained molecular data.

        We worked together with subject-matter experts to defined and evaluate a standardized clinical process, designed adequate tool support, and integrated relevant data source for an improved tumor board experience in molecular tumor boards. For example, we applied the scrum board approach, which is well-known from software engineering, to the clinical challenge.

        Background

        Molecular analysis of tumors is the key driver of precision medicine in oncology. It enables the matching of mutations to targeted drugs and hence more personalized treatments. With its steady advances and ever decreasing costs, it is likely to become a standard procedure for diagnosis and treatment. In molecular tumor boards, regular meetings among medical experts, treatment options are discussed and suggested for each patient based on the tumor’s genetic markup. A molecular tumor board (MTB) consists of 4 major phases: preparation, meeting, treatment suggestion documentation and follow-up. Our MTB Support Engine aims to streamline the entire process in order to tackle the key challenges of molecular tumor boards. The tool lends the Scrum methodology that has already proven itself in software development.

        The most critical part of a MTB is the preparation. Genetic variants need to be researched upon available medication. For this purpose, oncologists query various databases and search engines. This is time-consuming and therefore constitutes a key bottleneck for a comprehensive rollout. The MTB support engine accelerates the preparatory research work by

        1. Showing available information resources for a given variant
        2. Ranking and visualizing variants by various criteria and displaying most important ones first.
        3. Simplifying the saving of reviewed information in a click-and-collect fashion

        The MTB Support Engine furthermore lets doctors generate a presentation view of relevant information with a single click. In addition, an emphasis it put on collaborative work. Every participant has access to the system allowing preparing themselves. During the meeting, decided upon treatment suggestions can be documented for inspection by the treating physician and allows a structured follow-up.

        Most importantly, all information including researched annotations are saved. This allows the rapid retrieval of historic cases with genetically similar patients. Leveraging data integrated from multiple hospitals, even the discovery of novel correlations between variants and diseases is made possible in the long run.

        Related Content

        Project Partners

        Publications

        Research Publications

        • Llorca, I., Borchert, F., Schapranow, M.-P.: A Meta-dataset of German Medical Corpora: Harmonization of Annotations and Cross-corpus NER Evaluation. Proceedings of the 5th Clinical Natural Language Processing Workshop. bll. 171–181. Association for Computational Linguistics, Toronto, Canada (2023).
        • Kämmer, N., and Borchert, F., and Winkler, S., and de Melo, G., and Schapranow, M.-P.: Resolving Elliptical Compounds in German Medical Text. The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks. bll. 292–305. Association for Computational Linguistics, Toronto, Canada (2023).
        • Steinwand, S., Borchert, F., Winkler, S., Schapranow, M.-P.: GGTWEAK: Gene Tagging with Weak Supervision for German Clinical Text. In: Juarez, J.M., Marcos, M., Stiglic, G., en Tucker, A. (reds.) Artificial Intelligence in Medicine. bll. 183–192. Springer Nature Switzerland, Cham (2023).
        • Schapranow, M.-P., Borchert, F., Bougatf, N., Hund, H., Eils, R.: Software-Tool Support for Collaborative, Virtual, Multi-Site Molecular Tumor Boards. SN Computer Science. 4, 358 (2023).
        • Ladas, N., Borchert, F., Franz, S., Rehberg, A., Strauch, N., Sommer, K.K., Marschollek, M., Gietzelt, M.: Programming techniques for improving rule readability for rule-based information extraction natural language processing pipelines of unstructured and semi-structured medical texts. Health Informatics Journal. 29, 14604582231164696 (2023).
        • Richter-Pechanski, P., Wiesenbach, P., Schwab, D.M., Kiriakou, C., He, M., Allers, M.M., Tiefenbacher, A.S., Kunz, N., Martynova, A., Spiller, N., Mierisch, J., Borchert, F., Schwind, C., Frey, N., Dieterich, C., Geis, N.A.: A Distributable German Clinical Corpus Containing Cardiovascular Clinical Routine Doctor’s Letters. Scientific Data. 10, 207 (2023).
        • 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).
        • 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).
        • 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).
        • Ganzinger, M., Schapranow, M.-P.: FAIRe Datennutzung: Erfahrungen aus Verbundprojekten. gesundhyte.de: Das Magazin für Digitale Gesundheit in Deutschland. 14, 57–61 (2021).
        • 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).

        Sponsors

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        Electronic Registry for Elderly Care Services (ERPEL)

        Click to download executive summary (German only).

        Executive Summary

        The portion of elderly people within the Germany population is steadily increasing.  As a result, the demand for adequate elderly care services is high. Currently, capacity planning and strategic decision taking is conducted by districts individually. As of today, this federated approach is lacking standardized analysis methods to create a holistic national view on the care topic.

        The Electronic Registry for Elderly Care Services in Germany (ERPEL) (German: Elektronisches Register für Pflege-Dienstleistungen in Deutschland (ERPEL)) forms a longitudinal database of care-specific  measures from individual districts to form a holistic national overview. For example, it contains details about available elderly care services, the current available capacity, demand for a specific service in a specific region.

        ERPEL allows the up-to-date quantification of offer and demand for elderly care services across geographical regions. Thus, it enables a standardized methodological approach for interactive data analysis and exploration to support demand planning.

        User groups of ERPEL: Family members, social worker and social planners, as well as care service providers (excerpt).

        Amongst others, we are addressing the requirements of the following user groups:

        • Family members, who struggle to find appropriate elderly care for their relatives,
        • Social workers, who want to offer guidance for elderly care services,
        • Social planners, who aim to support governmental decision-making through provision of latest data, and
        • Care service providers, who are interested in the current and future demand for strategic planning and investments.

         

        Big Medilytics Consortium

        Executive Summary

        BigMedilytics is an international collaboration between partners from academia and industry across Europe. It aims to transform Europe’s Healthcare sector by using state-of-the-art Big Data technologies to achieve breakthrough productivity in the sector by reducing cost, improving patient outcomes and delivering better access to healthcare facilities simultaneously, covering the entire Healthcare Continuum – from Prevention to Diagnosis, Treatment and Home Care throughout Europe.

        In particular, we are focusing on applying latest big data and machine learning technologies to the BigMedilytics project use case nephrology to measure and analyze clinical performance indicators, integrate predicitive models, and measure their impact on clinical routine.

        Project Partners

        • Philips Electronics Nederland B.V., Netherlands
        • Fundacion Pala La Investiogation Del Hospital Clinico De La Comunitat Valencia, Fundacion Incliva, Spain
        • Instituto Technologico De Informatica, Spain
        • ERASMUS Universitait Medisch Centrum Rotterdam, Netherlands
        • ACHMEA BV, Netherlands
        • GIE AXA, France
        • OPTIMEDIS AG, Germany
        • ATOS Spain SA, Spain
        • Nederlandse Organisatie voor Toegepast-natuurwetenschappelijk onderzoek TNO, Netherlands
        • Technische Universiteit Eindhoven, Netherlands
        • HUAWEI Technologies Düsseldorf GMBH, Germany
        • Royal College of Surgeons in Ireland, Ireland
        • Stockholms Lans Landsting, Sweden
        • National Center for Scientific Research “Demokritos”, Greece
        • Athens Technology Center SA, Greece
        • Rheinische Friedrich-Wilhelms-Universität Bonn Germany
        • Universidad Politecnica de Madrid, Spain
        • Servicio Madrileño de Salud, Spain
        • Medizinische Universität Wien, Austria
        • IBM Israel – Science and Technology Ltd., Israel
        • Institut Curie, France
        • Teknologian tutkimuskeskus VTT Oy, Finland
        • Deutsches Forschungszentrum für Künstliche Intelligenz GmbH, Germany
        • Charité – Universitätsmedizin Berlin, Germany
        • AOK Nordost – Die Gesundheitskasse, Germany
        • Universitätsklinikum Essen, Germany
        • University of Southampton, United Kingdom
        • my mhealth limited, United Kingdom
        • ASTRAZENECA UK LIMITED, United Kingdom
        • Onze Lieve Vrouwe Gasthuis, Netherlands
        • Stichting Elisabeth-TweeSteden Ziekenhuis, Netherlands
        • ERASMUS Universiteit Rotterdam, Netherlands
        • Privredno Drustvo za Pruzanje Usluga Istrazivanje | Razvoj Nissatech Innovation Centro Doo, Serbia
        • Hasso Plattner Institute and AnalyzeGenomes.com

        Related Content

        In the following, we assembled related content that might be of your particular interest.

        Events

        Publications

          Research Publications

          • Freitas da Cruz, H., Bergner, B., Konak, O., Schneider, F., Bode, P., Lempert, C., Schapranow, M.-P.: MORPHER – A Platform to Support Modeling of Outcome and Risk Prediction in Health Research. Proceedings of the 19th IEEE International Conference on Bioinformatics and Biomedicine. , Athens, Greece (2019).
          • Freitas da Cruz, H., Horschig, S., Nusshag, C., Schapranow, M.-P.: Knowledge Distillation from Machine Learning Models for Prediction of Hemodialysis Outcomes. International Journal On Advances in Life Sciences. 11, 33–43 (2019).
          • Freitas da Cruz, H., Pfahringer, B., Schneider, F., Meyer, A., Schapranow, M.-P.: External Validation of a Black-Box Clinical Predictive Model in Nephrology: Can Interpretability Methods Help Illuminate Performance Differences?. Proceedings of 17th Conference on Artificial Intelligence in Medicine. bll. 191–201 (2019).

          Sponsors

          • 2018-2021 European Commission in context of the H2020 research program.

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            HiGHmed Medical Informatics Consortium

            Executive Summary

            The HiGHmed consortium aims to develop and use innovative information infrastructures to increase the efficiency of clinical research and to swiftly translate research results into validated improvements of patient care. These aims are tightly connected with challenges to integrate and further develop solutions of innovative, internationally interoperable data integration and methods, with the aim to demonstrate their added value for health research and patient care. The concepts must be designed in a way that will help to develop sustainable structures and with the perspective for an easy roll-out to other hospitals. You might want to refer to the HiGHmed consoritum website for further details.

            Project Partners

            • Heidelberg University Hospital (UKL-HD)
            • University Medical Center Göttingen (UMG)
            • Hannover Medical School (MHH)
            • University Hospital Schleswig-Holstein / Kiel University (UKSH)
            • University Hospital Cologne (UKK)
            • Universitätsklinikum Würzburg (UKW) / Julius-Maximilians-Universität Würzburg (JMU)
            • Charité Universitätsmedizin Berlin
            • University of Münster (WWU) / University Hospital Münster (UKM)
            • TU Braunschweig (TU-BS)
            • TU Darmstadt (TUD)
            • Heilbronn University (HHN)
            • Helmholtz Center for Infection Research (HZI)
            • Robert Koch-Institut (RKI)
            • German Cancer Research Center (DKFZ)
            • HAWK Hochschule Hildesheim/Holzminden/Goettingen (HAWK-HHG)
            • Hochschule Hannover – University of Applied Sciences and Arts (HSH)
            • Ada Health GmbH
            • InterComponentWare AG (ICW)
            • NEC
            • Siemens Healthcare GmbH
            • Carl-Thiem-Klinikum Cottbus
            • Hasso Plattner Institute and AnalyzeGenomes.com

            Publications

            Research Publications

            • Llorca, I., Borchert, F., Schapranow, M.-P.: A Meta-dataset of German Medical Corpora: Harmonization of Annotations and Cross-corpus NER Evaluation. Proceedings of the 5th Clinical Natural Language Processing Workshop. bll. 171–181. Association for Computational Linguistics, Toronto, Canada (2023).
            • Kämmer, N., and Borchert, F., and Winkler, S., and de Melo, G., and Schapranow, M.-P.: Resolving Elliptical Compounds in German Medical Text. The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks. bll. 292–305. Association for Computational Linguistics, Toronto, Canada (2023).
            • Steinwand, S., Borchert, F., Winkler, S., Schapranow, M.-P.: GGTWEAK: Gene Tagging with Weak Supervision for German Clinical Text. In: Juarez, J.M., Marcos, M., Stiglic, G., en Tucker, A. (reds.) Artificial Intelligence in Medicine. bll. 183–192. Springer Nature Switzerland, Cham (2023).
            • Schapranow, M.-P., Borchert, F., Bougatf, N., Hund, H., Eils, R.: Software-Tool Support for Collaborative, Virtual, Multi-Site Molecular Tumor Boards. SN Computer Science. 4, 358 (2023).
            • Ladas, N., Borchert, F., Franz, S., Rehberg, A., Strauch, N., Sommer, K.K., Marschollek, M., Gietzelt, M.: Programming techniques for improving rule readability for rule-based information extraction natural language processing pipelines of unstructured and semi-structured medical texts. Health Informatics Journal. 29, 14604582231164696 (2023).
            • Richter-Pechanski, P., Wiesenbach, P., Schwab, D.M., Kiriakou, C., He, M., Allers, M.M., Tiefenbacher, A.S., Kunz, N., Martynova, A., Spiller, N., Mierisch, J., Borchert, F., Schwind, C., Frey, N., Dieterich, C., Geis, N.A.: A Distributable German Clinical Corpus Containing Cardiovascular Clinical Routine Doctor’s Letters. Scientific Data. 10, 207 (2023).
            • 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).
            • 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).
            • 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).
            • Ganzinger, M., Schapranow, M.-P.: FAIRe Datennutzung: Erfahrungen aus Verbundprojekten. gesundhyte.de: Das Magazin für Digitale Gesundheit in Deutschland. 14, 57–61 (2021).
            • 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).
            • 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).
            • 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).
            • 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).
            • 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).

            Events

            Sponsors

            BMBF Logo

            Join us to understand the “Code of Life”

            Back to school to understand the “Code of Life”. We are happy to invite you to “Code of Life”  Massive Open Online Course (MOOC) hosted by openHPI. The course starts on Nov 14, 2016 and is designed as an interactive set of daily lectures followed by tasks. Sign-up for free to attend this unique course and to get your personal exam certificate at the end of the course.