MII - Medical Informatics Initiative

Motivation:

The aim of the medical informatics initiative is to ensure that, in future, each doctor, patient and researcher has access to the information they require – while simultaneously allowing individuals to maintain control over their personal data.

This will lead to more precise diagnostics, and better treatment decisions. It will yield new insights for research and medicine. And it will improve patient care and help combat diseases more effectively. Specifically, the initiative is about harnessing the flood of data generated every single day in healthcare and research environments – to the benefit of individual patients, to better understand illnesses, and to tailor treatments to the needs of the individual.

The goal is to establish a way to exchange research and care data across university hospitals. If it proves successful – with the help of funding from BMBF – it will open up entirely new horizons. The solutions that will be developed are expected to create added value across the health system.

Description:

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 new opportunities for patient care and research; BMBF launched its medical informatics initiative to make the most of this transformation. In the initiative’s first phase, university hospitals and partner organisations will establish and link data integration centres. These centres will allow research and healthcare data to be aggregated and integrated across multiple entities and sites. At the same time, innovative IT solutions for specific medical applications will be developed to demonstrate the benefits of high-tech digital healthcare services and infrastructures.

Participating university hospitals and their partners have formed consortia. These are tasked with developing strategies for shared data use and exchange. They will subsequently establish the data integration centres, and create IT solutions for concrete use cases.

These activities are coordinated by a national steering committee (NSG), to ensure the interoperability of IT systems and data integration centres between the consortia. In addition, a dedicated coordination office supports cross-consortia collaboration. This office is managed jointly by TMF – Technology, Methods and Infrastructure for Networked Medical Research, MFT (Medizinischer Fakultätentag – an association of German medical faculties), and VUD (Verband der Universtitätsklinika Deutschlands, which represents 33 university hospitals in Germany).

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

Funding codes:
  • BMBF
Funding details:
No funding details specified

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Allgemein

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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

"fhircrackr": An R Package Unlocking Fast Healthcare Interoperability Resources for Statistical Analysis.
POLAR - Polypharmacy, Drug Interactions, Risks

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 … developed in the context of the German Medical Informatics Initiative, where 29 university hospitals agreed to the usage of the Health Level Seven Fast Healthcare Interoperability Resources (FHIR) standard for their newly established data integration centers. This standard is optimized to describe and exchange medical data but less suitable for standard statistical analysis which mostly requires tabular data formats. OBJECTIVES: The objective of this work is to establish a tool that makes FHIR data accessible for standard statistical analysis by providing means to retrieve and transform data from a FHIR server. The tool should be implemented in a programming environment known to most data analysts and offer functions with variable degrees of flexibility and automation catering to users with different levels of FHIR expertise. METHODS: We propose the fhircrackr framework, which allows downloading and flattening FHIR resources for data analysis. The framework supports different download and authentication protocols and gives the user full control over the data that is extracted from the FHIR resources and transformed into tables. We implemented it using the programming language R [1] and published it under the GPL-3 open source license. RESULTS: The framework was successfully applied to both publicly available test data and real-world data from several ongoing studies. While the processing of larger real-world data sets puts a considerable burden on computation time and memory consumption, those challenges can be attenuated with a number of suitable measures like parallelization and temporary storage mechanisms. CONCLUSION: The fhircrackr R package provides an open source solution within an environment that is familiar to most data scientists and helps overcome the practical challenges that still hamper the usage of EHR data for research.

Authors: J. Palm, F. A. Meineke, J. Przybilla, T. Peschel

Date Published: 25th Jan 2023

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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 … easy to build, their training requires a lot of data, storage, and resources, which may not be available for use by medical institutions or could be beyond the skillset of the people who most need these tools. This paper describes a data analysis and machine learning platform that takes advantage of high-performance computing infrastructure for medical diagnosis support applications. This platform is validated by re-training a previously published deep learning model (COVID-Net) on new data, where it is shown that the performance of the model is improved through large-scale hyperparameter optimisation that uncovered optimal training parameter combinations. The per-class accuracy of the model, especially for COVID-19 and pneumonia, is higher when using the tuned hyperparameters (healthy: 96.5%; pneumonia: 61.5%; COVID-19: 78.9%) as opposed to parameters chosen through traditional methods (healthy: 93.6%; pneumonia: 46.1%; COVID-19: 76.3%). Furthermore, training speed-up analysis shows a major decrease in training time as resources increase, from 207 min using 1 node to 54 min when distributed over 32 nodes, but highlights the presence of a cut-off point where the communication overhead begins to affect performance. The developed platform is intended to provide the medical field with a technical environment for developing novel portable artificial-intelligence-based tools for diagnosis support.

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

Date Published: 2023

Publication Type: Journal article

“fhircrackr”: An R package unlocking fast healthcare interoperability resources for statistical analysis
SMITH - Smart Medical Information Technology for Healthcare

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 … developed in the context of the German Medical Informatics Initiative, where 29 university hospitals agreed to the usage of the Health Level Seven Fast Healthcare Interoperability Resources (FHIR) standard for their newly established data integration centers. This standard is optimized to describe and exchange medical data but less suitable for standard statistical analysis which mostly requires tabular data formats. OBJECTIVES: The objective of this work is to establish a tool that makes FHIR data accessible for standard statistical analysis by providing means to retrieve and transform data from a FHIR server. The tool should be implemented in a programming environment known to most data analysts and offer functions with variable degrees of flexibility and automation catering to users with different levels of FHIR expertise. METHODS: We propose the fhircrackr framework, which allows downloading and flattening FHIR resources for data analysis. The framework supports different download and authentication protocols and gives the user full control over the data that is extracted from the FHIR resources and transformed into tables. We implemented it using the programming language R [1] and published it under the GPL-3 open source license. RESULTS: The framework was successfully applied to both publicly available test data and real-world data from several ongoing studies. While the processing of larger real-world data sets puts a considerable burden on computation time and memory consumption, those challenges can be attenuated with a number of suitable measures like parallelization and temporary storage mechanisms. CONCLUSION: The fhircrackr R package provides an open source solution within an environment that is familiar to most data scientists and helps overcome the practical challenges that still hamper the usage of EHR data for research.

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

Date Published: 2023

Publication Type: Journal article

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

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

POLAR – „POLypharmazie, Arzneimittelwechselwirkungen und Risiken“ – wie können Daten aus der stationären Krankenversorgung zur Beurteilung beitragen?
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Zusammenfassung Hintergrund Mit der zunehmenden Anzahl eingenommener Arzneimittel steigt die Prävalenz von Medikationsrisiken. Hierzu zählen beispielsweise Arzneimittelwechselwirkungen, welche erwünschte … und unerwünschte Wirkungen einzelner Arzneistoffe reduzieren aber auch verstärken können. Fragestellung Das Verbundvorhaben POLAR (POLypharmazie, Arzneimittelwechselwirkungen und Risiken) hat das Ziel, mit Methoden und Prozessen der Medizininformatikinitiative (MII) auf Basis von „Real World Data“ (stationärer Behandlungsdaten von Universitätskliniken) einen Beitrag zur Detektion von Medikationsrisiken bei Patient:innen mit Polymedikation zu leisten. Im Artikel werden die konkreten klinischen Probleme dargestellt und am konkreten Auswertebeispiel illustriert. Material und Methoden Konkrete pharmakologische Fragestellungen werden algorithmisch abgebildet und an 13 Datenintegrationszentren in verteilten Analysen ausgewertet. Eine wesentliche Voraussetzung für die Anwendung dieser Algorithmen ist die Kerndatensatzstruktur der MII, die auf internationale IT-, Interoperabilitäts- und Terminologiestandards setzt. Ergebnisse In POLAR konnte erstmals gezeigt werden, dass stationäre Behandlungsdaten standortübergreifend auf der Basis abgestimmter, interoperabler Datenaustauschformate datenschutzkonform für Forschungsfragen zu arzneimittelbezogenen Problemen nutzbar gemacht werden können. Schlussfolgerungen Als Zwischenstand in POLAR wird ein erstes vorläufiges Ergebnis einer Analyse gezeigt. Darüber hinaus werden allgemeinere technische, rechtliche, kommunikative Chancen und Herausforderungen dargestellt, wobei der Fokus auf dem Fall der Verwendung stationärer Behandlungsdaten als „Real World Data“ für die Forschung liegt.

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Date Published: 1st Sep 2022

Publication Type: Journal article

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 … steps to obfuscate privacy-sensitive information contained in real clinical documents, the true origin of all GRASCCO texts. Therefore, it is publicly shareable without any legal restrictions We also explore whether this corpus still represents common clinical language use by comparison with a real (non-shareable) clinical corpus we developed as a contribution to the Medical Informatics Initiative in Germany (MII) within the SMITH consortium. We find evidence that such a claim can indeed be made.

Editor:

Date Published: 17th Aug 2022

Publication Type: InProceedings

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 … steps to obfuscate privacy-sensitive information contained in real clinical documents, the true origin of all GRASCCO texts. Therefore, it is publicly shareable without any legal restrictions We also explore whether this corpus still represents common clinical language use by comparison with a real (non-shareable) clinical corpus we developed as a contribution to the Medical Informatics Initiative in Germany (MII) within the SMITH consortium. We find evidence that such a claim can indeed be made.

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

Date Published: 1st Aug 2022

Publication Type: InCollection

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

POLAR_WP_1.1_v4a_ReferencesConditionsToEncounter
POLAR - Polypharmacy, Drug Interactions, Risks

Allgemein

Es sind 165 neue Patienten mit den gleichen Patientendaten, aus denen der Datensatz POLAR_WP_1.1_v2 generiert wurde, nur dass zusätzlich jede Condition eine Referenz zum Encounter hat. Da der Encounter selbst auch eine Referenz auf die Condition hat, gibt es hier eine Kreisreferenz, deren Upload einige FHIR-Server ablehnen (z.B. Vonk).

POLAR_WP_1.1_v4a_ReferencesConditionsToEncounter

=

Condition : 330 Consent : 100 Encounter : 330 Medication : 10 MedicationAdministration : 165 ...

Creator: Alexander Strübing

Submitter: Alexander Strübing

Data file type: Clinical Data

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Original Polar Test Data
POLAR - Polypharmacy, Drug Interactions, Risks

Dies sind die originalen Polar-Testdaten. Die Patienten sind aus echten Fällen abgeleitet. 61 Patienten (Patient)

  • mit jeweils 1 Einrichtungskontakt (Encounter) und jeweils 1 - 3 Abteilungskontakten (Encounter)
  • mit Diagnosen (Condition)
  • mit Laborwerten (Observation)
  • mit Vitalwerten (Observation)
  • mit Prozeduren (Procedure)
  • mit Medikation (Medication, Medication Administration, Medication Statement)

Condition : 270 Encounter : 129 Medication : 242 MedicationAdministration : 165 ...

Creators: Alexander Strübing, Jan Erik Gewehr, Friederike Salman, Yvonne Lemke, Norman Freier, Steffen Härterich, Thomas Maulhardt, Martin Böker, Ingolf Cascorbi, Ruwen Böhm, Felix Rottmann, Claudia Bulin, Anna Böhmer, Ulrich Jaehde, Katharina Karsten Dafonte, Martin Coenen, Gunther Hartmann, Martin Fromm, Renke Maas, Pauline Dürr, Melanie Then, Wahram Andrikyan

Submitter: Alexander Strübing

Data file type: Not specified

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VHF-Testdaten
POLAR - Polypharmacy, Drug Interactions, Risks

Allgemein

Alle einzelnen Datensätze sind aufgeteilt in jeweils (maximal) 1000 Patienten, die jeweils als json.zip und ndjson verfügbar sind. Die json.zip und ndjson beinhalten jeweils bis zu 1000 Transaktion-Bundles mit jeweils 1 Patienten. Die gleichnamige Excel-Datei ist die Vorlage zum Generieren JSON-Dateien.

VHF-Testdaten_01

Ursprüngliche Vorhofflimmern (VHF) Testdaten sind Patienten

  • unterschiedlichen Alters (17 bis 83 Jahre, davon 3300 Patienten mit Alter > 65 Jahre)
  • mit ...

Creators: None

Submitter: Alexander Strübing

Data file type: Not specified

Download
POLAR_WP_1.x_v1_MixedTestCasesForAllWorkpackages
POLAR - Polypharmacy, Drug Interactions, Risks

Allgemein

Datensatz zum Testen der Polar Workpackages, die jeweils als json.zip und ndjson verfügbar sind. Die json.zip und ndjson beinhalten jeweils bis zu 1000 Transaktion-Bundles mit 1 Patienten. Die gleichnamige Excel-Datei ist die Vorlage zum Generieren JSON-Dateien.

POLAR_WP_1.x_v1_MixedTestCasesForAllWorkpackages

Der Datensatz enthält Testfälle für alle Workpackages. Er wird nach Bedarf immer wieder erweitert.

Creators: None

Submitter: Alexander Strübing

Data file type: Clinical Data

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POLAR_WP_1.1_v4b_ReferencesOnlyConditionsToEncounter
POLAR - Polypharmacy, Drug Interactions, Risks

Achtung

DIESE DATEN SIND NICHT KDS-KONFORM. DER VALIDATOR WÜRDE SIE ABLEHNEN.

Sie werden aber zum Testen gebraucht, da einige DIZ-FHIR-Server anscheinend mit solchen unvalidierten Daten bespielt wurden.

Allgemein

Es sind 165 neue Patienten mit den gleichen Patientendaten, aus denen der Datensatz POLAR_WP_1.1_v2 generiert wurde, nur dass hier jede Condition eine Referenz zum Encounter hat, aber der Encounter keine Referenz auf die Condition. Die letzte der beiden Referenzen müsste aber auf ...

Creators: None

Submitter: Alexander Strübing

Data file type: Clinical Data

Download
POLAR_WP_1.1_v3_MultipleEncountersOverlappingStartEnd
POLAR - Polypharmacy, Drug Interactions, Risks

Allgemein

Es sind 70 neue Patienten mit den gleichen Patientendaten, aus denen der Datensatz POLAR_WP_1.1_v2 generiert wurde. Jeder Patient hat entweder 2 oder 4 Encounter, wobei einer immer ein Einrichtungskontakt ist und ein weiterer Encounter als Part-Of ein Abteilungskontakt. Die beiden zusammengehörigen Encounter haben immer dieselben Start- und Endzeiten. Die zusätzlichen Encounter sind alle mit einer jeweils einer Diagnose verbunden, die im Polar Workpackage 1.1 nicht relevant sind. ...

Creators: None

Submitter: Alexander Strübing

Data file type: Clinical Data

Download
POLAR_WP_1.1_v2
POLAR - Polypharmacy, Drug Interactions, Risks

Allgemein

Datensätze sind aufgeteilt in jeweils 1650 Patienten, die jeweils als json.zip und ndjson verfügbar sind. Die json.zip und ndjson beinhalten jeweils 1000 Transaktion-Bundles mit 1 Patienten. Die gleichnamige Excel-Datei ist die Vorlage zum Generieren JSON-Dateien.

POLAR_WP_1.1_v2

Generierte Daten aus im Grunde nur 165 Patienten (für jede Diagnose 1 Patient), die verhundertfacht wurden. Details siehe unten.

165 verschiedene Patienten (Patient)

  • mit jeweils 1 Einrichtungskontakt ...

Creators: None

Submitter: Alexander Strübing

Data file type: Not specified

Download
FHIR Data Resource Scheme
POLAR - Polypharmacy, Drug Interactions, Risks

Image of main FHIR resources with core data set (KDS) compliant linking.

Creators: None

Submitter: Alexander Strübing

Data file type: Image Data

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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

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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

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GraSCCo - Graz Synthetic Clinical text Corpus
SMITH - Smart Medical Information Technology for Healthcare
No description specified

Creator: Christina Lohr

Submitter: Christina Lohr

FHIR Data Resource Scheme
POLAR - Polypharmacy, Drug Interactions, Risks

Image of main FHIR resources with core data set (KDS) compliant linking.

Creator: Alexander Strübing

Submitter: Alexander Strübing

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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

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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
Wahram Andrikyan

Projects: POLAR - Polypharmacy, Drug Interactions, Risks

Institutions: Friedrich-Alexander Universität Erlangen-Nürnberg

Christoph Beger adminproject_administratorasset_housekeeper

Projects: LHA - Leipzig Health Atlas, Onto-Med Research Group, SMITH - Smart Medical Information Technology for Healthcare, Task Force COVID-19 Leipzig, NFDI4Health, LIFE Child, LIFE - Leipzig Research Center for Civilization Diseases, Project Test Demonstrator

Institutions: Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Universitätsklinikum Leipzig, AöR, invalid

https://orcid.org/0000-0002-1166-0368
Claudia Bulin

Projects: POLAR - Polypharmacy, Drug Interactions, Risks

Institutions: University Medical Center Schleswig-Holstein

Ruwen Böhm

Projects: POLAR - Polypharmacy, Drug Interactions, Risks

Institutions: University Medical Center Schleswig-Holstein

Anna Böhmer

Projects: POLAR - Polypharmacy, Drug Interactions, Risks

Institutions: Universität Bonn

Ingolf Cascorbi

Projects: POLAR - Polypharmacy, Drug Interactions, Risks

Institutions: University Medical Center Schleswig-Holstein

Martin Coenen

Projects: POLAR - Polypharmacy, Drug Interactions, Risks

Institutions: Universitätsklinikum Bonn, AöR

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

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

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