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228 Publications visible to you, out of a total of 228
Rückverfolgbarkeit für künstliche Intelligenz: Umsetzung einer Traceability-Strategie bei der modellgetriebenen Entwicklung klinischer Entscheidungsunterstützungssysteme
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Einleitung: Systeme auf Basis von Algorithmen mit \glqqkünstlicher Intelligenz\grqq werden im Gesundheitswesen zunehmend praktisch eingesetzt. Durch die Medizininformatik-Initiative sollen zukünftig … Daten aus Krankenversorgung und Forschung besser zugänglich werden. In diesem Rahmen[zum vollständigen Text gelangen Sie über die oben angegebene URL]

Authors: Lo Phan-Vogtmann an , Henner M. Kruse, Alexander Helhorn, Andrew J. Heidel, Eric Thomas, Kutaiba Saleh, André Scherag, Danny Ammon

Date Published: 8th Sep 2019

Publication Type: InProceedings

Modeling a Graph Data Model for FHIR Resources
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Introduction: (Fast Healthcare Interoperability Resources) is a modern standard for communication and representation of clinical data, where each dataset is defined by a single resource, linked to other … resources [ref:1]. But FHIR\circledR does explicitely not define how to persist these resources[for full text, please go to the a.m. URL]

Authors: Henner M. Kruse, Alexander Helhorn, Lo Phan-Vogtmann an , Eric Thomas, Andrew J. Heidel, Kutaiba Saleh, André Scherag, Danny Ammon

Date Published: 6th Sep 2019

Publication Type: InProceedings

Annotating German Clinical Documents for De-Identification.
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

We devised annotation guidelines for the de-identification of German clinical documents and assembled a corpus of 1,106 discharge summaries and transfer letters with 44K annotated protected health … information (PHI) items. After three iteration rounds, our annotation team finally reached an inter-annotator agreement of 0.96 on the instance level and 0.97 on the token level of annotation (averaged pair-wise F1 score). To establish a baseline for automatic de-identification on our corpus, we trained a recurrent neural network (RNN) and achieved F1 scores greater than 0.9 on most major PHI categories.

Authors: T. Kolditz, C. Lohr, J. Hellrich, L. Modersohn, B. Betz, M. Kiehntopf, U. Hahn

Date Published: 21st Aug 2019

Publication Type: InProceedings

The PI3K and MAPK/p38 pathways control stress granule assembly in a hierarchical manner
SMITH - Smart Medical Information Technology for Healthcare

Abstract

Not specified

Authors: Alexander Martin Heberle, Patricia Razquin Navas, Miriam Langelaar-Makkinje, Katharina Kasack, Ahmed Sadik, Erik Faessler, Udo Hahn, Philip Marx-Stoelting, Christiane A Opitz, Christine Sers, Ines Heiland, Sascha Schäuble, Kathrin Thedieck

Date Published: 28th Mar 2019

Publication Type: Journal article

An integrative web platform for user-friendly application of risk prediction models in hereditary cancer predisposition
GC-HBOC - German Consortium for Hereditary Breast and Ovarian Cancer

Abstract (Expand)

Background and Objective: Predicting individual mutation and cancer risks is essential to assist genetic counsellors in clinical decision making for patients with a hereditary cancer predisposition. … Worldwide a variety of statistical models and empirical data for risk prediction have been developed and published for hereditary breast and ovarian cancer (HBOC), and hereditary non-polyposis colorectal cancer (HNPCC / Lynch syndrome, LS). However, only few models have so far been implemented in convenient and easy-to-use computer applications. We therefore aimed to develop user-friendly applications of selected HBOC and LS risk prediction models, and to make them available through the "Leipzig Health Atlas" (LHA), a web-based multifunctional platform to share research data, novel ontologies, models and software tools with the medical and scientific community. LHA is a project funded within the BMBF initiative "i:DSem – Integrative data semantics in system medicine". Methods and Results: We selected a total of six statistical models and empirical datasets relevant for HBOC and LS: 1) the Manchester Scoring System, 2) the "Mutation Frequency Explorer" of the German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC), 3) an extended version of the Claus model, 4) MMRpredict, 5) PREMM1,2,6, and 6) PREMM5. The Manchester Scoring System allows calculation of BRCA1/2 mutation probabilities based on aggregated family history. The "Mutation Frequency Explorer" allows flexible assessment of mutation risks in BRCA1/2 and other genes for different sets of familial cancer histories based on a large dataset from the GC-HBOC. The extended Claus model (as implemented in the commercial predigree drawing software Cyrillic 2.1.3, which is no longer supported and no longer works on newer operating systems) predicts both mutation and breast cancer risks based on structured pedigree data. MMRpredict, PREMM 1,2,6, and PREMM 5 predict mutation risks in mismatch repair genes for patients from families suspected of having LS. All models were implemented using the statistical software "R" and the R-package "Shiny". "Shiny" allows the development of interactive applications by incorporating "R" with HTML and other web technologies. The Shiny apps are accessible on the website of the "Leipzig Health Atlas" (https://www.health-atlas.de) for registered researchers and genetic counselors. Conclusions: The risk prediction apps allow convenient calculation of mutation or cancer risks for an advice-seeking individual based on pedigree data or aggregated information on the familial cancer history. Target users should be specialized health professionals (physicians and genetic counselors) and scientists to ensure correct handling of the tools and careful interpretation of results.

Authors: Silke Zachariae, Sebastian Stäubert, C. Fischer, Markus Löffler, Christoph Engel

Date Published: 8th Mar 2019

Publication Type: InProceedings

Human Diseases: hereditary breast ovarian cancer syndrome, Lynch syndrome, colorectal cancer

Abgleich technischer Spezifikationen bei der Datentransformation proprietärer Formate in den Interoperabilitätsstandard HL7 FHIR
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Einleitung: Im Zuge der fortschreitenden Digitalisierung im Gesundheitswesen wird dem Thema der Interoperabilität zwischen allen medizinischen IT-Verfahren eine entscheidende Relevanz zugesprochen. Die … Grundlage hierfür bieten gemeinsame Informationsmodelle, wie sie im Rahmen der Medizininformatik-Initiative[zum vollständigen Text gelangen Sie über die oben angegebene URL]

Authors: Alexander Helhorn, Kutaiba Saleh, Henner M. Kruse, Lo Phan-Vogtmann an , Eric Thomas, Andrew J. Heidel, André Scherag, Danny Ammon

Date Published: 2019

Publication Type: Misc

Der Kerndatensatz der Medizininformatik-Initiative – Interoperable Spezifikation am Beispiel der Laborbefunde mittels LOINC und FHIR.
SMITH - Smart Medical Information Technology for Healthcare

Abstract

Not specified

Authors: D. Ammon, A. Bietenbeck, M. Boeker, T. Ganslandt, S. Heckmann, K. Heitmann, U. Sax, J. Schepers, S. C. Semler, S. Thun, S. Zautke

Date Published: 2019

Publication Type: Misc

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The Health Atlas - Local Data Hub/Leipzig is an alliance of medical ontologists, medical systems biologists and clinical trials groups to design and implement a multi-functional and quality-assured atlas. It provides models, data and metadata on specific use cases from medical research fields.

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