@misc{12816, abstract = {{Medical images need annotations with high-level semantic descriptors, so that domain experts can search for the desired dataset among an enormous volume of visual media within a Medical Data Integration Center. This article introduces a processing pipeline for storing and annotating DICOM and PNG imaging data by applying Elasticsearch, S3 and Deep Learning technologies. The proposed method processes both DICOM and PNG images to generate annotations. These image annotations are indexed in Elasticsearch with the corresponding raw data paths, where they can be retrieved and analyzed.}}, author = {{Cheng, Ka Yung and Pazmino, Santiago and Bergh, Bjoern and Lange-Hegermann, Markus and Schreiweis, Bjorn}}, booktitle = {{19th World Congress on Medical and Health Informatics (MEDINFO)}}, isbn = {{978-1-64368-456-7}}, issn = {{1879-8365}}, keywords = {{Medical image retrieval, data lake, DICOM, deep learning, elasticsearch}}, location = {{Sydney, AUSTRALIA}}, pages = {{1388--1389}}, publisher = {{IOS Press, Incorporated}}, title = {{{An Image Retrieval Pipeline in a Medical Data Integration Center.}}}, doi = {{10.3233/SHTI231208}}, volume = {{310}}, year = {{2024}}, } @article{11558, abstract = {{Patient Reported Outcomes (PROs) provide essential clinical data for the diagnosis and treatment of patients. Mobile technologies enable rapid and structured collection of PROs with a high usability. MoPat is an electronic PRO system developed at the Münster University that enables patients to complete PROs in multiple languages. This research reports the further development of MoPat and the inclusion of features to document images electronically that will be evaluated in a multi-site clinical research.}}, author = {{Soto-Rey, Iñaki and Hardt, Tobias and Hollenberg, Luca and Bruland, Philipp and Ständer, Sonja and Dugas, Martin and Storck, Michael}}, issn = {{1879-8365}}, journal = {{Stud Health Technol Inform}}, keywords = {{Electronic documentation, Mobile Health, Patient-reported Outcomes.}}, pages = {{1779--1780}}, publisher = {{IOS Press}}, title = {{{Electronic Image Documentation of Patient Reported Outcomes Using Mobile Technologies.}}}, doi = {{10.3233/SHTI190644}}, volume = {{264}}, year = {{2019}}, } @article{11569, author = {{Kock-Schoppenhauer, AK and Bruland, Philipp and Kadioglu, D and Brammen, D and Ulrich, H and Kulbe, K and Duhm-Harbeck, P and Ingenerf, J}}, issn = {{0926-9630}}, journal = {{Stud Health Technol Inform}}, pages = {{1516--1517}}, title = {{{Scientific Challenge in eHealth: MAPPATHON, a Metadata Mapping Challenge.}}}, doi = {{10.3233/SHTI190512}}, volume = {{264}}, year = {{2019}}, } @article{11561, abstract = {{CDISC's Operational Data Model (ODM) is a flexible standard for exchanging and archiving metadata and subject clinical data in clinical trials. The Portal of Medical Data Models (MDM-Portal) uses ODM to store more than 15000 medical forms. As not every electronic health system accepts ODM as input format, there is a need for conversion between ODM and other data standards and formats. This research proposes a standardised template-based process to develop ODM converters. So far, ten converters have been developed and integrated in the MDM-Portal following this process and new ones should be included soon. The template, programming utilities and an ODM test suite have been made online available and can be used to easily develop new converters.}}, author = {{Soto-Rey, I and Neuhaus, P and Bruland, Philipp and Geßner, S and Varghese, J and Hegselmann, S and Brix, T and Dugas, M and Storck, M}}, issn = {{1879-8365}}, journal = {{Stud Health Technol Inform}}, keywords = {{CDISC ODM, Semantics, interoperability}}, pages = {{231--235}}, title = {{{Standardising the Development of ODM Converters: The ODMToolBox.}}}, volume = {{247}}, year = {{2018}}, } @article{11577, abstract = {{The establishment of a digital healthcare system is a national and community task. The Federal Ministry of Education and Research in Germany is providing funding for consortia consisting of university hospitals among others participating in the "Medical Informatics Initiative". Exchange of medical data between research institutions necessitates a place where meta information for this data is made accessible. Within these consortia different metadata registry solutions were chosen. To promote interoperability between these solutions, we have examined whether the portal of Medical Data Models is eligible for managing and communicating metadata and relevant information across different data integration centres of the Medical Informatics Initiative and beyond. Apart from the MDM-portal, some ISO 11179-based systems such as Samply.MDR as well as openEHR-based solutions are going to be applyed. In this paper, we have focused on the creation of a mapping model between the CDISC ODM standard and the Samply.MDR import format. In summary, it can be stated that the mapping model is feasible and promote the exchangeability between different metadata registry approaches.}}, author = {{Kock-Schoppenhauer, AK and Ulrich, H and Wagen-Zink, S and Duhm-Harbeck, P and Ingenerf, J and Neuhaus, P and Dugas, M and Bruland, Philipp}}, issn = {{1879-8365}}, journal = {{Studies in health technology and informatics}}, keywords = {{CDISC ODM, MDR, data elements, mapping, metadata registry}}, pages = {{221--225}}, publisher = {{ IOS Press}}, title = {{{Compatibility Between Metadata Standards: Import Pipeline of CDISC ODM to the Samply.MDR.}}}, volume = {{247}}, year = {{2018}}, } @article{11560, abstract = {{To address current key problems of medical documentation: lack of transparency, overwhelming amount of medical contents to be documented and missing interoperability, the Portal of Medical Data Models (http://medical-data-models.org/) was established in 2012. Constantly evolving, four years later, the portal displays more than 8900 medical data models with more than 250000 items, of which 84 % have been semantically annotated with UMLS codes to support interoperability. Giving an update on new functions and contents of the portal, two additional export formats have been implemented, allowing the reuse of forms such as HL7's framework Fast Health Interoperability Resources (FHIR) Questionnaires, as well as the OpenDataKit format. Future projects include the implementation of an ODMtoOpenClinica converter, as well as supporting the reuse of forms with Apple's ResearchKit and Android's ResearchStack.}}, author = {{Geßner, S and Neuhaus, P and Varghese, J and Bruland, Philipp and Meidt, A and Soto-Rey, I and Storck, M and Doods, J and Dugas, M}}, issn = {{0926-9630}}, journal = {{Stud Health Technol Inform}}, keywords = {{Clinical Trial, Semantics, Surveys and Questionnaires}}, pages = {{858--862}}, publisher = {{ IOS Press}}, title = {{{The Portal of Medical Data Models: Where Have We Been and Where Are We Going?}}}, volume = {{245}}, year = {{2017}}, } @article{11576, abstract = {{Data dictionaries provide structural meta-information about data definitions in health information technology (HIT) systems. In this regard, reusing healthcare data for secondary purposes offers several advantages (e.g. reduce documentation times or increased data quality). Prerequisites for data reuse are its quality, availability and identical meaning of data. In diverse projects, research data warehouses serve as core components between heterogeneous clinical databases and various research applications. Given the complexity (high number of data elements) and dynamics (regular updates) of electronic health record (EHR) data structures, we propose a clinical metadata warehouse (CMDW) based on a metadata registry standard. Metadata of two large hospitals were automatically inserted into two CMDWs containing 16,230 forms and 310,519 data elements. Automatic updates of metadata are possible as well as semantic annotations. A CMDW allows metadata discovery, data quality assessment and similarity analyses. Common data models for distributed research networks can be established based on similarity analyses.}}, author = {{Bruland, Philipp and Doods, J and Storck, M and Dugas, M}}, issn = {{1879-8365}}, journal = {{Studies in health technology and informatics}}, keywords = {{Information Systems, Metadata, Semantics}}, pages = {{313--317}}, title = {{{What Information Does Your EHR Contain? Automatic Generation of a Clinical Metadata Warehouse (CMDW) to Support Identification and Data Access Within Distributed Clinical Research Networks.}}}, volume = {{245}}, year = {{2017}}, } @article{11590, abstract = {{Reading centers provide centralized high-quality diagnostics in ophthalmic clinical trials. Since ophthalmic images are captured in electronic format at peripheral clinics, an integrated workflow for image transfer and creation of structured reports is needed, including quality assurance. The image portal and the study database are separate components. We assessed whether this integration is feasible with trial-related IT standards and built a prototype system as a proof-of-concept. CDISC ODM and OAuth authentication were used to integrate the image portal with x4T-EDC, facilitating automatic data transfer and single sign-on.}}, author = {{Bruland, Philipp and Kathöfer, Ulrike and Treder, Maximilian and Eter, Nicole and Dugas, Martin}}, issn = {{1879-8365}}, journal = {{Studies in health technology and informatics}}, keywords = {{Electronic Data Capture, Reading Center, System Integration}}, pages = {{1254}}, publisher = {{IOS Press}}, title = {{{Integrating x4T-EDC into an Image-Portal to Establish an Ophthalmic Reading Center.}}}, volume = {{245}}, year = {{2017}}, } @article{11575, abstract = {{In the treatment of chronic pruritus-related, scratch-induced skin lesions the categorization, counting and temporal comparison are common methodologies. The observation requires a good memory and expertise in this field to gain comparable findings for this time-consuming process. Digital image processing aims at supporting such manual detections. The objective is to develop a software tool for automatic image detection and comparison. The new photographic setting implies the usage of markers to derive the brightness and size of lesions. MATLAB has been used for the software development. The newly defined setting allows taking standardized images of pruritus-associated cutaneous lesions for detection and comparison. The tool named PIACS (Prurigo Image Analyzing and Comparing System) allows automatically detecting, categorizing and comparing lesions based on digital images.}}, author = {{Bruland, Philipp and Hänse, Waldemar and Schedel, Fiona and Ständer, Sonja and Fritz, Fleur}}, issn = {{1879-8365}}, journal = {{Studies in health technology and informatics}}, pages = {{1042}}, publisher = {{IOS Press}}, title = {{{PIACS: A System for the Automatic Detection, Categorization and Comparison of Scratch-Related Skin Lesions in Dermatology.}}}, volume = {{216}}, year = {{2015}}, } @article{11573, abstract = {{Planning case report forms for data capture in clinical trials is a labor-insensitive and not formalized process. These CRFs are often neither standardized nor using defined data elements. Metadata registries as the NCI caDSR provide the capability to create forms based on common data elements. However, an exchange of these forms into clinical trial management systems through a standardized format like CDISC ODM is currently not offered. Thus, our objectives were to develop a mapping model between NCI forms and ODM. We analyzed 3012 NCI forms and included common data elements regarding their frequency and uniqueness. In this paper, we have created a mapping model between both formats and identified limitations in the conversion process: Semantic codes requested from the caDSR registry did not allow a proper mapping to ODM items and information like the number of module repetitions got lost. Summarized, it can be stated that our mapping model is feasible. However, mapping of semantic concepts in ODM needs to be specified more precisely.}}, author = {{Bruland, Philipp and Breil, Bernhard and Fritz, Fleur and Dugas, Martin}}, issn = {{1879-8365}}, journal = {{Studies in health technology and informatics}}, pages = {{564--568}}, publisher = {{IOS Press}}, title = {{{Interoperability in clinical research: from metadata registries to semantically annotated CDISC ODM.}}}, volume = {{180}}, year = {{2012}}, } @article{11596, author = {{Breil, B and Watermann, A and Haas, P and Dziuballe, P and Dugas, M}}, issn = {{0926-9630}}, journal = {{Stud Health Technol Inform}}, pages = {{1102--1104}}, title = {{{Semantic enrichment of medical forms - semi-automated coding of ODM-elements via web services.}}}, volume = {{180}}, year = {{2012}}, } @article{11597, author = {{Dziuballe, P and Forster, C and Breil, B and Thiemann, V and Fritz, F and Lechtenbörger, J and Vossen, G and Dugas, M}}, issn = {{0926-9630}}, journal = {{Stud Health Technol Inform}}, pages = {{902--906}}, title = {{{The single source architecture x4T to connect medical documentation and clinical research.}}}, volume = {{169}}, year = {{2011}}, }