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Published year: 20217
Use of a quantitative data report in a hypothetical decision scenario for health policymaking: a computer-assisted laboratory study

Abstract (Expand)

Background: Quantitative data reports are widely produced to inform health policy decisions. Policymakers are expected to critically assess provided information in order to incorporate the best available … evidence into the decision-making process. Many other factors are known to influence this process, but little is known about how quantitative data reports are actually read. We explored the reading behavior of (future) health policy decision-makers, using innovative methods. Methods: We conducted a computer-assisted laboratory study, involving starting and advanced students in medicine and health sciences, and professionals as participants. They read a quantitative data report to inform a decision on the use of resources for long-term care in dementia in a hypothetical decision scenario. Data were collected through eye-tracking, questionnaires, and a brief interview. Eye-tracking data were used to generate ‘heatmaps’ and five measures of reading behavior. The questionnaires provided participants’ perceptions of understandability and helpfulness as well as individual characteristics. Interviews documented reasons for attention to specific report sections. The quantitative analysis was largely descriptive, complemented by Pearson correlations. Interviews were analyzed by qualitative content analysis. Results: In total, 46 individuals participated [students (85%), professionals (15%)]. Eye-tracking observations showed that the participants spent equal time and attention for most parts of the presented report, but were less focused when reading the methods section. The qualitative content analysis identified 29 reasons for attention to a report section related to four topics. Eye-tracking measures were largely unrelated to participants’ perceptions of understandability and helpfulness of the report. Conclusions: Eye-tracking data added information on reading behaviors that were not captured by questionnaires or interviews with health decision-makers.

Authors: Pamela Wronski, Michel Wensing, Sucheta Ghosh, Lukas Gärttner, Wolfgang Müller, Jan Koetsenruijter

Date Published: 1st Dec 2021

Publication Type: Journal

Combining dictionary- and rule-based approximate entity linking with tuned BioBERT

Abstract (Expand)

Chemical named entity recognition (NER) is a significant step for many downstream applications like entity linking for the chemical text-mining pipeline. However, the identification of chemical entities … in a biomedical text is a challenging task due to the diverse morphology of chemical entities and the different types of chemical nomenclature. In this work, we describe our approach that was submitted for BioCreative version 7 challenge Track 2, focusing on the ‘Chemical Identification’ task for identifying chemical entities and entity linking, using MeSH. For this purpose, we have applied a two-stage approach as follows (a) usage of fine-tuned BioBERT for identification of chemical entities (b) semantic approximate search in MeSH and PubChem databases for entity linking. There was some friction between the two approaches, as our rule-based approach did not harmonise optimally with partially recognized words forwarded by the BERT component. For our future work, we aim to resolve the issue of the artefacts arising from BERT tokenizers and develop joint learning of chemical named entity recognition and entity linking using pre-trained transformer-based models and compare their performance with our preliminary approach. Next, we will improve the efficiency of our approximate search in reference databases during entity linking. This task is non-trivial as it entails determining similarity scores of large sets of trees with respect to a query tree. Ideally, this will enable flexible parametrization and rule selection for the entity linking search.

Authors: Ghadeer Mobasher, Lukrécia Mertová, Sucheta Ghosh, Olga Krebs, Bettina Heinlein, Wolfgang Müller

Date Published: 11th Nov 2021

Publication Type: Proceedings

Watching people decide: decision prediction using heatmaps of reading of a decision-support document

Abstract

Not specified

Authors: Sucheta Ghosh, Pamela Wronski, Jan Koetsenruijter, Wolfgang Mueller, Michel Wensing

Date Published: 27th Sep 2021

Publication Type: Journal

Implementing FAIR data management within the German Network for Bioinformatics Infrastructure (de.NBI) exemplified by selected use cases.

Abstract (Expand)

This article describes some use case studies and self-assessments of FAIR status of de.NBI services to illustrate the challenges and requirements for the definition of the needs of adhering to the FAIR … (findable, accessible, interoperable and reusable) data principles in a large distributed bioinformatics infrastructure. We address the challenge of heterogeneity of wet lab technologies, data, metadata, software, computational workflows and the levels of implementation and monitoring of FAIR principles within the different bioinformatics sub-disciplines joint in de.NBI. On the one hand, this broad service landscape and the excellent network of experts are a strong basis for the development of useful research data management plans. On the other hand, the large number of tools and techniques maintained by distributed teams renders FAIR compliance challenging.

Authors: G. Mayer, W. Muller, K. Schork, J. Uszkoreit, A. Weidemann, U. Wittig, M. Rey, C. Quast, J. Felden, F. O. Glockner, M. Lange, D. Arend, S. Beier, A. Junker, U. Scholz, D. Schuler, H. A. Kestler, D. Wibberg, A. Puhler, S. Twardziok, J. Eils, R. Eils, S. Hoffmann, M. Eisenacher, M. Turewicz

Date Published: 2nd Sep 2021

Publication Type: Journal

NFDI4Health – Nationale Forschungsdateninfrastruktur für personenbezogene Gesundheitsdaten

Abstract (Expand)

Epidemiologische und klinische Studien sind standardisiert und gut dokumentiert, jedoch erfüllen Studienprotokolle, eingesetzte Erhebungsinstrumente und erhobene Daten die Anforderungen der FAIR-Prinzipien … nicht in ausreichendem Maße. NFDI4Health wird daher eine Struktur schaffen, die eine zentrale Suche nach existierenden, dezentral verwalteten Datenkörpern und zugehörigen Dokumenten sowie einen FAIRen Zugang zu diesen erleichtert. Dazu werden die Auffindbarkeit und der Zugang zu strukturierten Gesundheitsdaten aus Registern, administrativen Gesundheitsdatenbanken, klinischen und epidemiologischen sowie Public Health-Studien verbessert und die Qualität und Harmonisierung der zugrundeliegenden Daten optimiert. Eine weitere Herausforderung entsteht durch die Verwendung personenbezogener Gesundheitsdaten. Diese sind hoch sensibel, so dass ihre Nutzung restriktive Datenschutzbestimmungen und informierte Einwilligungserklärungen der Studienteilnehmenden erfordert, was jedoch ihre Wiederverwendbarkeit einschränkt. NFDI4Health zielt daher darauf ab, den Austausch und die Verknüpfung von personenbezogenen Gesundheitsdaten sowie verteilte Datenanalysen unter Einhaltung datenschutzrechtlicher und ethischer Bestimmungen zu erleichtern. Um dies möglichst effizient zu erreichen, wird NFDI4Health die Entwicklung neuer, maschinenprozessierbarer Zustimmungsmöglichkeiten sowie innovativer Datenzugriffsservices auf Grundlage der FAIR-Prinzipien vorantreiben und die Interoperabilität von IT-Lösungen für Metadatenrepositorien stärken. Komplementiert wird dies durch die Entwicklung entsprechender Angebote für Training und Ausbildung, um der Herausforderung der Umsetzung der Lösungen in den Universitäten und Forschungseinrichtungen zu begegnen. Schließlich wird durch die gemeinsame Arbeit in der NFDI4Health die Kooperation zwischen klinischer und epidemiologischer/Public Health-Forschung gestärkt.

Authors: Juliane Fluck, Birte Lindstädt, Wolfgang Ahrens, Oya Beyan, Benedikt Buchner, Johannes Darms, Ralf Depping, Jens Dierkes, Hubertus Neuhausen, Wolfgang Müller, Hajo Zeeb, Martin Golebiewski, Markus Löffler, Matthias Löbe, Frank Meineke, Sebastian Klammt, Holger Fröhlich, Horst Hahn, Matthias Schulze, Tobias Pischon, Ute Nöthlings, Ulrich Sax, Harald Kusch, Linus Grabenhenrich, Carsten Oliver Schmidt, Dagmar Waltemath, Sebastian Semler, Juliane Gehrke, Toralf Kirsten, Fabian Praßer, Sylvia Thun, Lothar Wieler, Iris Pigeot

Date Published: 28th Jul 2021

Publication Type: Misc

PEtab-Interoperable specification of parameter estimation problems in systems biology.

Abstract (Expand)

Reproducibility and reusability of the results of data-based modeling studies are essential. Yet, there has been-so far-no broadly supported format for the specification of parameter estimation problems … in systems biology. Here, we introduce PEtab, a format which facilitates the specification of parameter estimation problems using Systems Biology Markup Language (SBML) models and a set of tab-separated value files describing the observation model and experimental data as well as parameters to be estimated. We already implemented PEtab support into eight well-established model simulation and parameter estimation toolboxes with hundreds of users in total. We provide a Python library for validation and modification of a PEtab problem and currently 20 example parameter estimation problems based on recent studies.

Authors: L. Schmiester, Y. Schalte, F. T. Bergmann, T. Camba, E. Dudkin, J. Egert, F. Frohlich, L. Fuhrmann, A. L. Hauber, S. Kemmer, P. Lakrisenko, C. Loos, S. Merkt, W. Muller, D. Pathirana, E. Raimundez, L. Refisch, M. Rosenblatt, P. L. Stapor, P. Stadter, D. Wang, F. G. Wieland, J. R. Banga, J. Timmer, A. F. Villaverde, S. Sahle, C. Kreutz, J. Hasenauer, D. Weindl

Date Published: 27th Jan 2021

Publication Type: Journal

FAST, ADAPTED, CURATED FAIR DATA FOR COVID-19 RESEARCH

Abstract (Expand)

Research projects such as the international COVID-19 Disease Map initiative and the German COVID-19 study hub of NFDI are supported by de.NBI-SysBio tools and services in organizing and sharing research … data ’FAIRly‘. This is done via the data management platform FAIRDOMHub/SEEK which is quickly adapted to the users' needs. COVID-19 related literature is manually curated and used for basic research about the curation process of SABIO-RK to provide the research community with high quality kinetics data.

Authors: Maja Rey, Andreas Weidemann, Ulrike Wittig, Dorotea Dudas, Sucheta Ghosh, Martin Golebiewski, Xiaoming Hu, Wolfgang Müller

Date Published: 2021

Publication Type: Booklet

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