SMITH - Smart Medical Information Technology for Healthcare

Medical data routinely generated in everyday clinical practice is processed and made available to medical research in a standardized form. Patients benefit from reliable research results, more precise diagnoses and better treatments. In order to link data from care and research, the participating university hospitals in Aachen, Bonn, Essen, Halle, Hamburg, Jena and Leipzig have established sustainable Data Integration Centers. The network partners Ruhr University Bochum, the Düsseldorf University Hospital and the University Medical Center Rostock are preparing the structure.

Programme: MII - Medical Informatics Initiative

LHA ID: 81CN2DP7WT-9

Funding codes:

01ZZ1803A

Public web page: https://www.smith.care/

Human Diseases: No Human Disease specified

Health Atlas - Local Data Hub/Leipzig PALs: Markus Löffler

Project Coordinators: No Project coordinators for this Project

Project start date: 1st Jan 2018

Project Hierarchy

Related items

Averbis GmbH Freiburg

Country: Germany

City: Freiburg i. Br.

Web page: https://averbis.com/de/

Bayer AG Leverkusen

Country: Germany

City: Leverkusen

Web page: https://www.bayer.com/de/

Forschungszentrum Jülich GmbH

Country: Germany

City: Jülich

Web page: https://www.fz-juelich.de

Fraunhofer-Institut für Software- und Systemtechnik ISST

Country: Germany

City: Dortmund

Web page: https://www.isst.fraunhofer.de

Friedrich-Schiller-Universität Jena

Country: Germany

City: Jena

Web page: https://www.uni-jena.de

ID Information und Dokumentation im Gesundheitswesen GmbH & Co. KGaA

Country: Germany

City: Berlin

Web page: https://www.id-berlin.de

Institute for Medical Informatics, Statistics and Epidemiology (IMISE)

Country: Germany

City: Leipzig

Web page: https://www.imise.uni-leipzig.de/en/

März Internetwork Services AG

Country: Germany

City: Essen

Web page: https://www.maerz-network.de

RWTH Aachen University

Country: Germany

City: Aachen

Web page: https://www.rwth-aachen.de

University of Leipzig

Country: Germany

City: Leipzig

Web page: https://www.uni-leipzig.de/en/

View all 20 items

Methodical Use Case PheP - NLP

Methodical Use Case PheP

(Show All)

One of the challenges of Phenotypingt is that too little clinical information is available as machine-readable data sets. Admission letters, findings and operating room reports in particular contain valuable information such as diagnoses, medications, side effects and laboratory data that can only be extracted using methods of natural language processing and semantic text analysis methods. Natural Language Processing (NLP) is used to process documents from the Hospital Information System (HIS). ...

Submitter: Frank A. Meineke

Studies: No Studies

Resources: No Resources

Snapshots: No snapshots

Created: 2nd Feb 2023 at 11:56

Methodical Use Case PheP - Pipeline

Methodical Use Case PheP

PheP is a platform that enables clinical researchers to work together with statisticians and computer scientists in interdisciplinary collaboration to pursue scientific issues that previously seemed economically and technologically unthinkable. For this purpose, it is necessary to build data sets that can be used for clinical-epidemiological and health-economic issues.

From phenotypes, i.e. determinable characteristics of patients, further characteristics can be derived and provided via phenotyping. ...

Submitter: Frank A. Meineke

Studies: No Studies

Resources: No Resources

Snapshots: No snapshots

Created: 26th Jan 2023 at 09:41, Last updated: 2nd Feb 2023 at 11:54

Ontology-based Phenotyping

SMITH - Smart Medical Information Technology for Healthcare, LHA - Leipzig Health Atlas, Onto-Med Research Group

No description specified

Submitter: Alexandr Uciteli

Studies: Ontological Modelling of Basic Eligibility Criteria, Ontological Modelling of Type 2 Diabetes Mellitus (T2DM) Phenotype, PhenoMan Evaluation with Synthetic FHIR Data

Resources: Basic Eligibility Criteria of an Example Blood Pressure Study, Ontological Modelling of T2DM Phenotype using Phenotype Manager (PhenoMan), PhenoMan Evaluation - Synthetic FHIR Data

Snapshots: No snapshots

Created: 9th Mar 2020 at 12:34, Last updated: 29th Jun 2020 at 14:50

PhenoMan Evaluation with Synthetic FHIR Data

SMITH - Smart Medical Information Technology for Healthcare, LHA - Leipzig Health Atlas, Onto-Med Research Group

We evaluated if the PhenoMan returns correct/complete result sets and if it is working with real data. To simulate a FHIR health data store with real data we used Synthea(TM) to generate a large data set and imported it into a HAPI FHIR JPA Server. Based on the synthetic data set we developed ten example queries with different structure and complexity with PhenoMan and SQL. We compared the results of the queries in means of execution time and equality of results.

The detailed steps of the evaluation ...

Submitter: Christoph Beger

Investigation: Ontology-based Phenotyping

Resources: PhenoMan Evaluation - Synthetic FHIR Data

Study type: Not specified

Snapshots: No snapshots

Created: 29th Jun 2020 at 14:53, Last updated: 29th Jun 2020 at 15:31

Ontological Modelling of Basic Eligibility Criteria

SMITH - Smart Medical Information Technology for Healthcare, LHA - Leipzig Health Atlas, Onto-Med Research Group

No description specified

Submitter: Alexandr Uciteli

Investigation: Ontology-based Phenotyping

Resources: Basic Eligibility Criteria of an Example Blood Pressure Study

Study type: Not specified

Snapshots: No snapshots

Created: 30th Apr 2020 at 12:02, Last updated: 30th Apr 2020 at 12:02

Ontological Modelling of Type 2 Diabetes Mellitus (T2DM) Phenotype

SMITH - Smart Medical Information Technology for Healthcare, LHA - Leipzig Health Atlas, Onto-Med Research Group

No description specified

Submitter: Alexandr Uciteli

Investigation: Ontology-based Phenotyping

Resources: Ontological Modelling of T2DM Phenotype using Phenotype Manager (PhenoMan)

Study type: Not specified

Snapshots: No snapshots

Created: 9th Mar 2020 at 15:12, Last updated: 9th Mar 2020 at 15:14

PhenoMan Evaluation - Synthetic FHIR Data

SMITH - Smart Medical Information Technology for Healthcare, LHA - Leipzig Health Atlas, Onto-Med Research Group

This assay bundles all synthetic FHIR data of the PhenoMan evaluation. The data arised from a subset of a Synthea(TM) generated data set. We truncated some resource types like Encounter and Provider to reduce the size of the data set and to speed up the import in a FHIR health data store.

Submitter: Christoph Beger

Resource type: Result Dataset of Clinical Study

Technology type: Technology Type

Investigation: Ontology-based Phenotyping

Study: PhenoMan Evaluation with Synthetic FHIR Data

Human Diseases: asthma, bronchial disease, hypertension, obesity

Data files: PhenoMan Evaluation - AllergyIntolerance FHIR R..., PhenoMan Evaluation - Condition FHIR Resources, PhenoMan Evaluation - Observation FHIR Resources, PhenoMan Evaluation - Patient FHIR Resources

Snapshots: No snapshots

Created: 29th Jun 2020 at 14:58, Last updated: 30th Jun 2020 at 15:49

Basic Eligibility Criteria of an Example Blood Pressure Study

SMITH - Smart Medical Information Technology for Healthcare, LHA - Leipzig Health Atlas, Onto-Med Research Group

No description specified

Submitter: Alexandr Uciteli

Biological problem addressed: Model Analysis Type

Investigation: Ontology-based Phenotyping

Study: Ontological Modelling of Basic Eligibility Crit...

Human Diseases: No human diseases

Models: No Models

Data files: Eligibility Criteria Ontology for an Example Bl...

Snapshots: No snapshots

Created: 30th Apr 2020 at 12:06, Last updated: 30th Apr 2020 at 12:30

Ontological Modelling of T2DM Phenotype using Phenotype Manager (PhenoMan)

SMITH - Smart Medical Information Technology for Healthcare, LHA - Leipzig Health Atlas, Onto-Med Research Group

We modelled the algorithm for determining Type 2 Diabetes Mellitus (T2DM) cases presented by PheKB.org using Phenotype Manager (PhenoMan).

Submitter: Alexandr Uciteli

Biological problem addressed: Model Analysis Type

Investigation: Ontology-based Phenotyping

Study: Ontological Modelling of Type 2 Diabetes Mellit...

Human Diseases: diabetes mellitus

Models: No Models

Data files: T2DM Case 1 Reasoner Report, T2DM Case 2 Reasoner Report, T2DM Case 3 Reasoner Report, T2DM Case 4 Reasoner Report, T2DM Case 5 Reasoner Report, T2DM Graphical Representation, T2DM Ontology, T2DM Tabular Representation

Snapshots: No snapshots

Created: 9th Mar 2020 at 15:18, Last updated: 10th Mar 2020 at 12:00

Medical multivariate time series imputation and forecasting based on a recurrent conditional Wasserstein GAN and attention

SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

OBJECTIVE: In the fields of medical care and research as well as hospital management, time series are an important part of the overall data basis. To ensure high quality standards and enable suitable … decisions, tools for precise and generic imputations and forecasts that integrate the temporal dynamics are of great importance. Since forecasting and imputation tasks involve an inherent uncertainty, the focus of our work lay on a probabilistic multivariate generative approach that samples infillings or forecasts from an analysable distribution rather than producing deterministic results. MATERIALS AND METHODS: For this task, we developed a system based on generative adversarial networks that consist of recurrent encoders and decoders with attention mechanisms and can learn the distribution of intervals from multivariate time series conditioned on the periods before and, if available, periods after the values that are to be predicted. For training, validation and testing, a data set of jointly measured blood pressure series (ABP) and electrocardiograms (ECG) (length: 1,250=ˆ10s) was generated. For the imputation tasks, one interval of fixed length was masked randomly and independently in both channels of every sample. For the forecasting task, all masks were positioned at the end. RESULTS: The models were trained on around 65,000 bivariate samples and tested against 14,000 series of different persons. For the evaluation, 50 samples were produced for every masked interval to estimate the range of the generated infillings or forecasts. The element-wise arithmetic average of these samples served as an estimator for the mean of the learned conditional distribution. The approach showed better results than a state-of-the-art probabilistic multivariate forecasting mechanism based on Gaussian copula transformation and recurrent neural networks. On the imputation task, the proposed method reached a mean squared error (MSE) of 0.057 on the ECG channel and an MSE of 28.30 on the ABP channel, while the baseline approach reached MSEs of 0.095 (ECG) and 229.1 (ABP). Moreover, on the forecasting task, the presented system achieved MSEs of 0.069 (ECG) and 33.73 (ABP), outperforming the recurrent copula approach, which reached MSEs of 0.082 (ECG) and 196.53 (ABP). CONCLUSION: The presented generative probabilistic system for the imputation and forecasting of (medical) time series features the flexibility to handle masks of different sizes and positions, the ability to quantify uncertainty due to its probabilistic predictions, and an adjustable trade-off between the goals of minimising errors in individual predictions and minimising the distance between the learned and the real conditional distribution of the infillings or forecasts.

Authors: Sven Festag, Cord Spreckelsen

Date Published: 1st Feb 2023

Publication Type: Journal article

Citation: J. Biomed. Inform. 139:104320

Created: 24th Feb 2023 at 17:05

Analysis of chest X-ray for COVID-19 diagnosis as a use case for an HPC-enabled data analysis and machine learning platform for medical diagnosis support

SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

The COVID-19 pandemic shed light on the need for quick diagnosis tools in healthcare, leading to the development of several algorithmic models for disease detection. Though these models are relatively …

Authors: Chadi Barakat, Marcel Aach, Andreas Schuppert, Sigur\dhur Brynjólfsson, Sebastian Fritsch, Morris Riedel

Date Published: 2023

Publication Type: Journal article

Citation: Diagnostics (Basel) 13(3):391

Created: 24th Feb 2023 at 17:05

“fhircrackr”: An R package unlocking fast healthcare interoperability resources for statistical analysis

SMITH - Smart Medical Information Technology for Healthcare

(Show All)

Abstract (Expand)

BACKGROUND: The growing interest in the secondary use of electronic health record (EHR) data has increased the number of new data integration and data sharing infrastructures. The present work has been …

Authors: Julia Palm, Frank A Meineke, Jens Przybilla, Thomas Peschel

Date Published: 2023

Publication Type: Journal article

Citation: Appl. Clin. Inform. 14(1):54–64

Created: 24th Feb 2023 at 17:05

A FHIR has been lit on gICS: facilitating the standardised exchange of informed consent in a large network of university medicine

SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

BACKGROUND: The Federal Ministry of Education and Research of Germany (BMBF) funds a network of university medicines (NUM) to support COVID-19 and pandemic research at national level. The “COVID-19 … Data Exchange Platform” (CODEX) as part of NUM establishes a harmonised infrastructure that supports research use of COVID-19 datasets. The broad consent (BC) of the Medical Informatics Initiative (MII) is agreed by all German federal states and forms the legal base for data processing. All 34 participating university hospitals (NUM sites) work upon a harmonised infrastructural as well as legal basis for their data protection-compliant collection and transfer of their research dataset to the central CODEX platform. Each NUM site ensures that the exchanged consent information conforms to the already-balloted HL7 FHIR consent profiles and the interoperability concept of the MII Task Force “Consent Implementation” (TFCI). The Independent Trusted Third-Party (TTP) of the University Medicine Greifswald supports data protection-compliant data processing and provides the consent management solutions gICS. METHODS: Based on a stakeholder dialogue a required set of FHIR-functionalities was identified and technically specified supported by official FHIR experts. Next, a “TTP-FHIR Gateway” for the HL7 FHIR-compliant exchange of consent information using gICS was implemented. A last step included external integration tests and the development of a pre-configured consent template for the BC for the NUM sites. RESULTS: A FHIR-compliant gICS-release and a corresponding consent template for the BC were provided to all NUM sites in June 2021. All FHIR functionalities comply with the already-balloted FHIR consent profiles of the HL7 Working Group Consent Management. The consent template simplifies the technical BC rollout and the corresponding implementation of the TFCI interoperability concept at the NUM sites. CONCLUSIONS: This article shows that a HL7 FHIR-compliant and interoperable nationwide exchange of consent information could be built using of the consent management software gICS and the provided TTP-FHIR Gateway. The initial functional scope of the solution covers the requirements identified in the NUM-CODEX setting. The semantic correctness of these functionalities was validated by project-partners from the Ludwig-Maximilian University in Munich. The production rollout of the solution package to all NUM sites has started successfully.

Authors: Martin Bialke, Lars Geidel, Christopher Hampf, Arne Blumentritt, Peter Penndorf, Ronny Schuldt, Frank-Michael Moser, Stefan Lang, Patrick Werner, Sebastian Stäubert, Hauke Hund, Fady Albashiti, Jürgen Gührer, Hans-Ulrich Prokosch, Thomas Bahls, Wolfgang Hoffmann

Date Published: 1st Dec 2022

Publication Type: Journal article

Citation: BMC Med. Inform. Decis. Mak. 22(1):335

Created: 24th Feb 2023 at 17:05

Application of convex hull analysis for the evaluation of data heterogeneity between patient populations of different origin and implications of hospital bias in downstream machine-learning-based data processing: A comparison of 4 critical-care patient datasets

SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Machine learning (ML) models are developed on a learning dataset covering only a small part of the data of interest. If model predictions are accurate for the learning dataset but fail for unseen data … then generalization error is considered high. This problem manifests itself within all major sub-fields of ML but is especially relevant in medical applications. Clinical data structures, patient cohorts, and clinical protocols may be highly biased among hospitals such that sampling of representative learning datasets to learn ML models remains a challenge. As ML models exhibit poor predictive performance over data ranges sparsely or not covered by the learning dataset, in this study, we propose a novel method to assess their generalization capability among different hospitals based on the convex hull (CH) overlap between multivariate datasets. To reduce dimensionality effects, we used a two-step approach. First, CH analysis was applied to find mean CH coverage between each of the two datasets, resulting in an upper bound of the prediction range. Second, 4 types of ML models were trained to classify the origin of a dataset (i.e., from which hospital) and to estimate differences in datasets with respect to underlying distributions. To demonstrate the applicability of our method, we used 4 critical-care patient datasets from different hospitals in Germany and USA. We estimated the similarity of these populations and investigated whether ML models developed on one dataset can be reliably applied to another one. We show that the strongest drop in performance was associated with the poor intersection of convex hulls in the corresponding hospitals’ datasets and with a high performance of ML methods for dataset discrimination. Hence, we suggest the application of our pipeline as a first tool to assess the transferability of trained models. We emphasize that datasets from different hospitals represent heterogeneous data sources, and the transfer from one database to another should be performed with utmost care to avoid implications during real-world applications of the developed models. Further research is needed to develop methods for the adaptation of ML models to new hospitals. In addition, more work should be aimed at the creation of gold-standard datasets that are large and diverse with data from varied application sites.

Authors: Konstantin Sharafutdinov, Jayesh S Bhat, Sebastian Johannes Fritsch, Kateryna Nikulina, Moein E Samadi, Richard Polzin, Hannah Mayer, Gernot Marx, Johannes Bickenbach, Andreas Schuppert

Date Published: 1st Oct 2022

Publication Type: Journal article

Citation: Front. Big Data 5:603429

Created: 24th Feb 2023 at 17:05

POLAR – „POLypharmazie, Arzneimittelwechselwirkungen und Risiken“ – wie können Daten aus der stationären Krankenversorgung zur Beurteilung beitragen?

SMITH - Smart Medical Information Technology for Healthcare

(Show All)

Abstract (Expand)

Zusammenfassung Hintergrund Mit der zunehmenden Anzahl eingenommener Arzneimittel steigt die Prävalenz von Medikationsrisiken. Hierzu zählen beispielsweise Arzneimittelwechselwirkungen, welche erwünschte …

Authors: André Scherag, Wahram Andrikyan, Tobias Dreischulte, Pauline Dürr, Martin F Fromm, Jan Gewehr, Ulrich Jaehde, Miriam Kesselmeier, Renke Maas, Petra A Thürmann, Frank Meineke, Daniel Neumann, Julia Palm, Thomas Peschel, Editha Räuscher, Susann Schulze, Torsten Thalheim, Thomas Wendt, Markus Loeffler, D Ammon, W Andrikyan, U Bartz, B Bergh, T Bertsche, O Beyan, S Biergans, H Binder, M Boeker, H Bogatsch, R Böhm, A Böhmer, J Brandes, C Bulin, D Caliskan, I Cascorbi, M Coenen, F Dietz, F Dörje, T Dreischulte, J Drepper, P Dürr, A Dürschmid, F Eckelt, R Eils, A Eisert, C Engel, F Erdfelder, K Farker, M Federbusch, S Franke, N Freier, T Frese, M Fromm, K Fünfgeld, T Ganslandt, J Gewehr, D Grigutsch, W Haefeli, U Hahn, A Härdtlein, R Harnisch, S Härterich, M Hartmann, R Häuslschmid, C Haverkamp, O Heinze, P Horki, M Hug, T Iskra, U Jaehde, S Jäger, P Jürs, C Jüttner, J Kaftan, T Kaiser, K Karsten Dafonte, M Kesselmeier, S Kiefer, S Klasing, O Kohlbacher, D Kraska, S Krause, S Kreutzke, R Krock, K Kuhn, S Lederer, M Lehne, M Löbe, M Loeffler, C Lohr, V Lowitsch, N Lüneburg, M Lüönd, I Lutz, R Maas, U Mansmann, K Marquardt, A Medek, F Meineke, A Merzweiler, A Michel-Backofen, Y Mou, B Mussawy, D Neumann, J Neumann, C Niklas, M Nüchter, K Oswald, J Palm, T Peschel, H Prokosch, J Przybilla, E Räuscher, L Redeker, Y Remane, A Riedel, M Rottenkolber, F Rottmann, F Salman, J Schepers, A Scherag, F Schmidt, S Schmiedl, K Schmitz, G Schneider, A Scholtz, S Schorn, B Schreiweis, S Schulze, A K Schuster, M Schwab, H Seidling, S Semler, K Senft, M Slupina, R Speer, S Stäubert, D Steinbach, C Stelzer, H Stenzhorn, M Strobel, T Thalheim, M Then, P Thürmann, D Tiller, P Tippmann, Y Ucer, S Unger, J Vogel, J Wagner, J Wehrle, D Weichart, L Weisbach, S Welten, T Wendt, R Wettstein, I Wittenberg, R Woltersdorf, M Yahiaoui-Doktor, S Zabka, S Zenker, S Zeynalova, L Zimmermann, D Zöller, für das POLAR-Projekt

Date Published: 1st Sep 2022

Publication Type: Journal article

Citation: Prävent. Gesundheitsförderung

Created: 24th Feb 2023 at 17:05, Last updated: 24th Feb 2023 at 17:56

GRASCCO — The First Publicly Shareable, Multiply-Alienated German Clinical Text Corpus

SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

We describe the creation of GRASCCO, a novel German-language corpus composed of some 60 clinical documents with more than.43,000 tokens. GRASCCO is a synthetic corpus resulting from a series of alienation …

Editor:

Date Published: 17th Aug 2022

Publication Type: InProceedings

PubMed ID: 36073490

DOI: 10.3233/SHTI220805

Citation: Modersohn L, Schulz S, Lohr C, Hahn U. GRASCCO - The First Publicly Shareable, Multiply-Alienated German Clinical Text Corpus. Stud Health Technol Inform. 2022 Aug 17;296:66-72. doi: 10.3233/SHTI220805. PMID: 36073490.

Created: 26th Jan 2023 at 15:46, Last updated: 30th Jan 2023 at 10:39

GRASCCO — the first publicly shareable, multiply-alienated German clinical text corpus

SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Authors: Luise Modersohn, Stefan Schulz, Christina Lohr, Udo Hahn

Date Published: 1st Aug 2022

Publication Type: InCollection

Citation: In Studies in Health Technology and Informatics of Studies in health technology and informatics, IOS Press

Created: 24th Feb 2023 at 17:05

Data protection-compliant broad consent for secondary use of health care data and human biosamples for (bio)medical research: Towards a new German national standard

SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

BACKGROUND: The secondary use of deidentified but not anonymized patient data is a promising approach for enabling precision medicine and learning health care systems. In most national jurisdictions … (e.g., in Europe), this type of secondary use requires patient consent. While various ethical, legal, and technical analyses have stressed the opportunities and challenges for different types of consent over the past decade, no country has yet established a national consent standard accepted by the relevant authorities. METHODS: A working group of the national Medical Informatics Initiative in Germany conducted a requirements analysis and developed a GDPR-compliant broad consent standard. The development included consensus procedures within the Medical Informatics Initiative, a documented consultation process with all relevant stakeholder groups and authorities, and the ultimate submission for approval via the national data protection authorities. RESULTS: This paper presents the broad consent text together with a guidance document on mandatory safeguards for broad consent implementation. The mandatory safeguards comprise i) independent review of individual research projects, ii) organizational measures to protect patients from involuntary disclosure of protected information, and iii) comprehensive information for patients and public transparency. This paper further describes the key issues discussed with the relevant authorities, especially the position on additional or alternative consent approaches such as dynamic consent. DISCUSSION: Both the resulting broad consent text and the national consensus process are relevant for similar activities internationally. A key challenge of aligning consent documents with the various stakeholders was explaining and justifying the decision to use broad consent and the decision against using alternative models such as dynamic consent. Public transparency for all secondary use projects and their results emerged as a key factor in this justification. While currently largely limited to academic medicine in Germany, the first steps for extending this broad consent approach to wider areas of application, including smaller institutions and medical practices, are currently under consideration.

Authors: Sven Zenker, Daniel Strech, Kristina Ihrig, Roland Jahns, Gabriele Müller, Christoph Schickhardt, Georg Schmidt, Ronald Speer, Eva Winkler, Sebastian Graf von Kielmansegg, Johannes Drepper

Date Published: 1st Jul 2022

Publication Type: Journal article

Citation: J. Biomed. Inform. 131(104096):104096

Created: 24th Feb 2023 at 17:05

GGPONC 2.0 — The German Clinical Guideline Corpus for Oncology: Curation Workflow, Annotation Policy, Baseline NER Taggers

SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

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 …

Editor: Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis

Date Published: 19th Jun 2022

Publication Type: Journal article

Citation: [GGPONC 2.0 - The German Clinical Guideline Corpus for Oncology: Curation Workflow, Annotation Policy, Baseline NER Taggers](https://aclanthology.org/2022.lrec-1.389) (Borchert et al., LREC 2022)

Created: 26th Jan 2023 at 15:43, Last updated: 26th Jan 2023 at 15:43

View all 84 items

T2DM Ontology

LHA - Leipzig Health Atlas, Onto-Med Research Group, SMITH - Smart Medical Information Technology for Healthcare

T2DM Phenotype Algorithm Specification Ontology (PASO) developed using PhenoMan

Creator: Alexandr Uciteli

Submitter: Alexandr Uciteli

Data file type: Not specified

Investigations: Ontology-based Phenotyping

Studies: Ontological Modelling of Type 2 Diabetes Mellit...

Resources: Ontological Modelling of T2DM Phenotype using P...

Download

Created: 9th Mar 2020 at 15:52, Last updated: 12th Aug 2020 at 15:23

T2DM Tabular Representation

LHA - Leipzig Health Atlas, Onto-Med Research Group, SMITH - Smart Medical Information Technology for Healthcare

The tabular representation of the T2DM phenotype algorithm generated by PhenoMan using the T2DM ontology

Creator: Alexandr Uciteli

Submitter: Alexandr Uciteli

Data file type: Not specified

Investigations: Ontology-based Phenotyping

Studies: Ontological Modelling of Type 2 Diabetes Mellit...

Resources: Ontological Modelling of T2DM Phenotype using P...

Download

Created: 9th Mar 2020 at 16:00, Last updated: 11th Aug 2020 at 14:14

Eligibility Criteria Ontology for an Example Blood Pressure Study

Onto-Med Research Group, SMITH - Smart Medical Information Technology for Healthcare

No description specified

Creator: Alexandr Uciteli

Submitter: Alexandr Uciteli

Data file type: Not specified

Investigations: Ontology-based Phenotyping

Studies: Ontological Modelling of Basic Eligibility Crit...

Resources: Basic Eligibility Criteria of an Example Blood ...

Download

Created: 30th Apr 2020 at 12:30, Last updated: 30th Apr 2020 at 12:30

T2DM Case 5 Reasoner Report

LHA - Leipzig Health Atlas, Onto-Med Research Group, SMITH - Smart Medical Information Technology for Healthcare

Example Reasoner Report generated by PhenoMan

Creator: Alexandr Uciteli

Submitter: Alexandr Uciteli

Data file type: Not specified

Investigations: Ontology-based Phenotyping

Studies: Ontological Modelling of Type 2 Diabetes Mellit...

Resources: Ontological Modelling of T2DM Phenotype using P...

Download

Created: 10th Mar 2020 at 12:00, Last updated: 10th Mar 2020 at 12:00

T2DM Case 4 Reasoner Report

LHA - Leipzig Health Atlas, Onto-Med Research Group, SMITH - Smart Medical Information Technology for Healthcare

Example Reasoner Report generated by PhenoMan

Creator: Alexandr Uciteli

Submitter: Alexandr Uciteli

Data file type: Not specified

Investigations: Ontology-based Phenotyping

Studies: Ontological Modelling of Type 2 Diabetes Mellit...

Resources: Ontological Modelling of T2DM Phenotype using P...

Download

Created: 10th Mar 2020 at 11:58, Last updated: 10th Mar 2020 at 11:58

T2DM Case 3 Reasoner Report

LHA - Leipzig Health Atlas, Onto-Med Research Group, SMITH - Smart Medical Information Technology for Healthcare

Example Reasoner Report generated by PhenoMan

Creator: Alexandr Uciteli

Submitter: Alexandr Uciteli

Data file type: Not specified

Investigations: Ontology-based Phenotyping

Studies: Ontological Modelling of Type 2 Diabetes Mellit...

Resources: Ontological Modelling of T2DM Phenotype using P...

Download

Created: 10th Mar 2020 at 11:57, Last updated: 10th Mar 2020 at 11:57

T2DM Case 2 Reasoner Report

LHA - Leipzig Health Atlas, Onto-Med Research Group, SMITH - Smart Medical Information Technology for Healthcare

Example Reasoner Report generated by PhenoMan

Creator: Alexandr Uciteli

Submitter: Alexandr Uciteli

Data file type: Not specified

Investigations: Ontology-based Phenotyping

Studies: Ontological Modelling of Type 2 Diabetes Mellit...

Resources: Ontological Modelling of T2DM Phenotype using P...

Download

Created: 10th Mar 2020 at 11:47, Last updated: 10th Mar 2020 at 11:47

T2DM Case 1 Reasoner Report

LHA - Leipzig Health Atlas, Onto-Med Research Group, SMITH - Smart Medical Information Technology for Healthcare

Example Reasoner Report generated by PhenoMan

Creator: Alexandr Uciteli

Submitter: Alexandr Uciteli

Data file type: Not specified

Investigations: Ontology-based Phenotyping

Studies: Ontological Modelling of Type 2 Diabetes Mellit...

Resources: Ontological Modelling of T2DM Phenotype using P...

Download

Created: 10th Mar 2020 at 11:45, Last updated: 10th Mar 2020 at 11:45

T2DM Graphical Representation

LHA - Leipzig Health Atlas, Onto-Med Research Group, SMITH - Smart Medical Information Technology for Healthcare

The decision tree was generated by PhenoMan in GraphML format and was processed using an automatic layout of the yEd Graph Editor. Nonetheless, it is also possible to generate the complete tree as image in PNG format.

Creator: Alexandr Uciteli

Submitter: Alexandr Uciteli

Data file type: Not specified

Investigations: Ontology-based Phenotyping

Studies: Ontological Modelling of Type 2 Diabetes Mellit...

Resources: Ontological Modelling of T2DM Phenotype using P...

Download

Created: 9th Mar 2020 at 16:08, Last updated: 9th Mar 2020 at 16:08

GraSCCo - Graz Synthetic Clinical text Corpus

SMITH - Smart Medical Information Technology for Healthcare

No description specified

Creator: Christina Lohr

Submitter: Christina Lohr

Model type: Not specified

Model format: Not specified

Environment: Not specified

Organism: Not specified

Human Disease: Not specified

Investigations: No Investigations

Studies: No Studies

Resources: No Resources

Created: 30th Jan 2023 at 10:44

Ontological Modelling and Reasoning of Phenotypes

LHA - Leipzig Health Atlas, SMITH - Smart Medical Information Technology for Healthcare, Onto-Med Research Group

Presentation on JOWO/ODLS 2019 in Graz

Creators: Alexandr Uciteli, Christoph Beger

Submitter: Christoph Beger

Download

Created: 7th Sep 2020 at 10:07, Last updated: 7th Sep 2020 at 10:07

Danny Ammon

Projects: SMITH - Smart Medical Information Technology for Healthcare, POLAR - Polypharmacy, Drug Interactions, Risks

Institutions: Universitätsklinikum Jena

https://orcid.org/0000-0001-8960-7316

Expertise: data sharing, Interoperability, data integration, medical informatics

Tools: fhir, IHE, HL7, 3LGM² Tool

As head of the Data Integration Center at Jena University Hospital, Dr. Danny Ammon is active in the areas of standardization, processing and communication of medical documentation for healthcare and biomedical research.

Philipp Daumke

Projects: SMITH - Smart Medical Information Technology for Healthcare

Institutions: Averbis GmbH Freiburg

Armin de Greiff

Projects: SMITH - Smart Medical Information Technology for Healthcare

Institutions: Universitätsklinikum Essen

Daniel Diekmann

Projects: SMITH - Smart Medical Information Technology for Healthcare

Institutions: ID Information und Dokumentation im Gesundheitswesen GmbH & Co. KGaA

Claudia Fischer

Projects: SMITH - Smart Medical Information Technology for Healthcare

Institutions: Universitätsklinikum Jena

Jan Erik Gewehr

Projects: SMITH - Smart Medical Information Technology for Healthcare, POLAR - Polypharmacy, Drug Interactions, Risks

Institutions: Universitätsklinikum Hamburg-Eppendorf (UKE)

Udo Hahn

Projects: SMITH - Smart Medical Information Technology for Healthcare

Institutions: Friedrich-Schiller-Universität Jena

René Hänsel

Projects: LHA - Leipzig Health Atlas, LIFE Adult, LIFE - Leipzig Research Center for Civilization Diseases, LIFE HNC - Head and Neck Cancer Group, LIFE Heart, MMML - Molecular mechanisms in malignant lymphoma, GLA - German Lymphoma Alliance, MMML Demonstrators - Molecular Mechanisms in Malignant Lymphomas - Demonstrators of Personalized Medicine, HaematoOpt - Individualized model-based managing of the next-cycle thrombopenia of CHOEP/CHOP treated patients based on platelets dynamics during the previous cycles, e:Med, GC-HBOC - German Consortium for Hereditary Breast and Ovarian Cancer, GC-HNPCC - German Consortium for Hereditary Non-Polyposis Colorectal Cancer, MMML-MYC-SYS, NLP4CR - Natural Language Processing for Clinical Research, Genetical Statistics and Systems Biology, SepNet - German Competence Network Sepsis, LIFE Child, HNPCC-Sys - Genomic and transcriptomic heterogeneity of colorectal tumours arising in Lynch syndrome, GGN - German Glioma Network, CAPSys - Footprints of Sepsis Framed Within Community Acquired Pneumonia in the Blood Transcriptome, CapSys - Systems Medicine of Community Acquired Pneumonia, ProstataCA, HaematoSys - Systems biology of haematopoiesis and haematopoietic neoplasia, SMITH - Smart Medical Information Technology for Healthcare, Task Force COVID-19 Leipzig, NFDI4Health, POLAR - Polypharmacy, Drug Interactions, Risks, Management of health information systems, LivSys Transfer - Transfer of the LivSys in vitro system for hepatotoxicity into application, Project Test Demonstrator, Fundus photography as tool for analysis of eyes of subjects with diabetes, Clinical Trials Leipzig, NFDI4Health - TA3 Services, STOP-NUC, SCALE-TORT

Institutions: Institute for Medical Informatics, Statistics and Epidemiology (IMISE)

https://orcid.org/0000-0001-8344-0658

Roles: Technician

Expertise: Data Management, Data analysis, Python, Html

Thomas Klöss

Projects: SMITH - Smart Medical Information Technology for Healthcare

Institutions: Universitätsklinikum Halle (Saale), AöR

View all 32 items

MII - Medical Informatics Initiative

The medical informatics initiative was created to close the gap between research and healthcare. All of Germany’s university hospitals have joined forces with research institutions, businesses, health insurers, and patient advocacy groups to create a framework that harnesses research findings to the direct benefit of patients. The German Federal Ministry of Education and Research (BMBF) is investing around 160 million euros in the programme through 2021. The digitisation of medicine is creating ...

Projects: Methodical Use Case PheP, SMITH - Smart Medical Information Technology for Healthcare, POLAR - Polypharmacy, Drug Interactions, Risks

Web page: https://www.medizininformatik-initiative.de/en

Joint Ontology Workshops (JOWO) 2019

The Joint Ontology WOrkshops (JOWO) is a venue of workshops that, together, address a wide spectrum of topics related to ontology research, ranging from Cognitive Science to Knowledge Representation, Natural Language Processing, Artificial Intelligence, Logic, Philosophy, and Linguistics.

Start Date: 23rd Sep 2019

End Date: 25th Sep 2019

Event Website: https://www.iaoa.org/jowo/2019/

Country: Austria

City: Graz

Created: 9th Mar 2020 at 12:07

SMITH - Smart Medical Information Technology for Healthcare

Navigate

Health Atlas - Local Data Hub/Leipzig

Registered Repository