Improving Eye-Tracking Data Quality: A Framework for Reproducible Evaluation of Detection Algorithms.

High-quality eye-tracking data are crucial in behavioral sciences and medicine. Even with a solid understanding of the literature, selecting the most suitable algorithm for a specific research project poses a challenge. Empowering applied researchers to choose the best-fitting detector for their research needs is the primary contribution of this paper. We developed a framework to systematically assess and compare the effectiveness of 13 state-of-the-art algorithms through a unified application interface. Hence, we more than double the number of algorithms that are currently usable within a single software package and allow researchers to identify the best-suited algorithm for a given scientific setup. Our framework validation on retrospective data underscores its suitability for algorithm selection. Through a detailed and reproducible step-by-step workflow, we hope to contribute towards significantly improved data quality in scientific experiments.

A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research.

Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings.

Modern Information Technology for Cancer Research: What's in IT for Me? An Overview of Technologies and Approaches.

Information technology (IT) can enhance or change many scenarios in cancer research for the better. In this paper, we introduce several examples, starting with clinical data reuse and collaboration including data sharing in research networks. Key challenges are semantic interoperability and data access (including data privacy). We deal with gathering and analyzing genomic information, where cloud computing, uncertainties and reproducibility challenge researchers. Also, new sources for additional phenotypical data are shown in patient-reported outcome and machine learning in imaging. Last, we focus on therapy assistance, introducing tools used in molecular tumor boards and techniques for computer-assisted surgery. We discuss the need for metadata to aggregate and analyze data sets reliably. We conclude with an outlook towards a learning health care system in oncology, which connects bench and bedside by employing modern IT solutions.

[Patient registries for rare diseases in Germany: concept paper of the NAMSE strategy group]. / Patientenregister für Seltene Erkrankungen in Deutschland: Konzeptpapier der Strategiegruppe "Register" des Nationalen Aktionsbündnisses für Menschen mit Seltenen Erkrankungen (NAMSE).

The National Action Plan for People with Rare Diseases contains 52 concrete actions, including in the fields of care, research, diagnosis, and information management. With the aim of improving the quality and interoperability of national registries in the long term, action 28 proposed the establishment of a "Rare Diseases Registry" strategy group. The strategy group began its work in 2016. In this report, the group takes into account developments at the national and international level in order to develop recommendations for national initiatives.In addition to this, the group reports on consent and implementation as well as on the adaptation of a minimal dataset for use in rare disease registries and mapping the used data elements and schemata in a metadata repository. This position paper was created by the strategy group together with additional authors. The paper reached a consensus within the strategy group and can be seen as a concept paper of the Rare Diseases Registry strategy group.

MAGIC: once upon a time in consent management-a FHIR® tale.

BACKGROUND: The use of medical data for research purposes requires an informed consent of the patient that is compliant with the EU General Data Protection Regulation. In the context of multi-centre research initiatives and a multitude of clinical and epidemiological studies scalable and automatable measures for digital consent management are required. Modular form, structure, and contents render a patient's consent reusable for varying project settings in order to effectively manage and minimise organisational and technical efforts. RESULTS: Within the DFG-funded project "MAGIC" (Grant Number HO 1937/5-1) the digital consent management service tool gICS was enhanced to comply with the recommendations published in the TMF data protection guideline for medical research. In addition, a structured exchange format for modular consent templates considering established standards and formats in the area of digital informed consent management was designed. Using the new FHIR standard and the HAPI FHIR library, the first version for an exchange format and necessary import-/export-functionalities were successfully implemented. CONCLUSIONS: The proposed exchange format is a "work in progress". It represents a starting point for current discussions concerning digital consent management. It also attempts to improve interoperability between different approaches within the wider IHE-/HL7-/FHIR community. Independent of the exchange format, providing the possibility to export, modify and import templates for consents and withdrawals to be reused in similar clinical and epidemiological studies is an essential precondition for the sustainable operation of digital consent management.

[Registries for rare diseases : OSSE - An open-source framework for technical implementation]. / Register für seltene Erkrankungen : OSSE ­ ein Open-Source-Framework für die technische Umsetzung.

Meager amounts of data stored locally, a small number of experts, and a broad spectrum of technological solutions incompatible with each other characterize the landscape of registries for rare diseases in Germany. Hence, the free software Open Source Registry for Rare Diseases (OSSE) was created to unify and streamline the process of establishing specific rare disease patient registries. The data to be collected is specified based on metadata descriptions within the registry framework's so-called metadata repository (MDR), which was developed according to the ISO/IEC 11179 standard. The use of a central MDR allows for sharing the same data elements across any number of registries, thus providing a technical prerequisite for making data comparable and mergeable between registries and promoting interoperability.With OSSE, the foundation is laid to operate linked patient registries while respecting strong data protection regulations. Using the federated search feature, data for clinical studies can be identified across registries. Data integrity, however, remains intact since no actual data leaves the premises without the owner's consent. Additionally, registry solutions other than OSSE can participate via the OSSE bridgehead, which acts as a translator between OSSE registry networks and non-OSSE registries. The pseudonymization service Mainzelliste adds further data protection.Currently, more than 10 installations are under construction in clinical environments (including university hospitals in Frankfurt, Hamburg, Freiburg and Münster). The feedback given by the users will influence further development of OSSE. As an example, the installation process of the registry for undiagnosed patients at University Hospital Frankfurt is described in more detail.

An international registry for primary ciliary dyskinesia.

Primary ciliary dyskinesia (PCD) is a rare autosomal recessive disorder leading to chronic upper and lower airway disease. Fundamental data on epidemiology, clinical presentation, course and treatment strategies are lacking in PCD. We have established an international PCD registry to realise an unmet need for an international platform to systematically collect data on incidence, clinical presentation, treatment and disease course.The registry was launched in January 2014. We used internet technology to ensure easy online access using a web browser under www.pcdregistry.eu. Data from 201 patients have been collected so far. The database is comprised of a basic data form including demographic and diagnostic information, and visit forms designed to monitor the disease course.To establish a definite PCD diagnosis, we used strict diagnostic criteria, which required two to three diagnostic methods in addition to classical clinical symptoms. Preliminary analysis of lung function data demonstrated a mean annual decline of percentage predicted forced expiratory volume in 1 s of 0.59% (95% CI 0.98-0.22).Here, we present the development of an international PCD registry as a new promising tool to advance the understanding of this rare disorder, to recruit candidates for research studies and ultimately to improve PCD care.

[Strategies for biobank networks. Classification of different approaches for locating samples and an outlook on the future within the BBMRI-ERIC]. / Strategien zur Vernetzung von Biobanken. Klassifizierung verschiedener Ansätze zur Probensuche und Ausblick auf die Zukunft in der BBMRI-ERIC.

BACKGROUND: Medical research projects often require more biological material than can be supplied by a single biobank. For this reason, a multitude of strategies support locating potential research partners with matching material without requiring centralization of sample storage. OBJECTIVES: Classification of different strategies for biobank networks, in particular for locating suitable samples. Description of an IT infrastructure combining these strategies. MATERIALS AND METHODS: Existing strategies can be classified according to three criteria: (a) granularity of sample data: coarse bank-level data (catalogue) vs. fine-granular sample-level data, (b) location of sample data: central (central search service) vs. decentral storage (federated search services), and (c) level of automation: automatic (query-based, federated search service) vs. semi-automatic (inquiry-based, decentral search). All mentioned search services require data integration. Metadata help to overcome semantic heterogeneity. RESULTS: The "Common Service IT" in BBMRI-ERIC (Biobanking and BioMolecular Resources Research Infrastructure) unites a catalogue, the decentral search and metadata in an integrated platform. As a result, researchers receive versatile tools to search suitable biomaterial, while biobanks retain a high degree of data sovereignty. CONCLUSIONS: Despite their differences, the presented strategies for biobank networks do not rule each other out but can complement and even benefit from each other.

A RESTful interface to pseudonymization services in modern web applications.

BACKGROUND: Medical research networks rely on record linkage and pseudonymization to determine which records from different sources relate to the same patient. To establish informational separation of powers, the required identifying data are redirected to a trusted third party that has, in turn, no access to medical data. This pseudonymization service receives identifying data, compares them with a list of already reported patient records and replies with a (new or existing) pseudonym. We found existing solutions to be technically outdated, complex to implement or not suitable for internet-based research infrastructures. In this article, we propose a new RESTful pseudonymization interface tailored for use in web applications accessed by modern web browsers. METHODS: The interface is modelled as a resource-oriented architecture, which is based on the representational state transfer (REST) architectural style. We translated typical use-cases into resources to be manipulated with well-known HTTP verbs. Patients can be re-identified in real-time by authorized users' web browsers using temporary identifiers. We encourage the use of PID strings for pseudonyms and the EpiLink algorithm for record linkage. As a proof of concept, we developed a Java Servlet as reference implementation. RESULTS: The following resources have been identified: Sessions allow data associated with a client to be stored beyond a single request while still maintaining statelessness. Tokens authorize for a specified action and thus allow the delegation of authentication. Patients are identified by one or more pseudonyms and carry identifying fields. Relying on HTTP calls alone, the interface is firewall-friendly. The reference implementation has proven to be production stable. CONCLUSION: The RESTful pseudonymization interface fits the requirements of web-based scenarios and allows building applications that make pseudonymization transparent to the user using ordinary web technology. The open-source reference implementation implements the web interface as well as a scientifically grounded algorithm to generate non-speaking pseudonyms.

Assessing the prognoses on Health care in the information society 2013--thirteen years after.

Health care and information technology in health care is advancing at tremendous speed. We analysed whether the prognoses by Haux et al. - first presented in 2000 and published in 2002 - have been fulfilled in 2013 and which might be the reasons for match or mismatch. Twenty international experts in biomedical and health informatics met in May 2013 in a workshop to discuss match or mismatch of each of the 71 prognoses. After this meeting a web-based survey among workshop participants took place. Thirty-three prognoses were assessed matching; they reflect e.g. that there is good progress in storing patient data electronically in health care institutions. Twenty-three prognoses were assessed mismatching; they reflect e.g. that telemedicine and home monitoring as well as electronic exchange of patient data between institutions is not established as widespread as expected. Fifteen prognoses were assessed neither matching nor mismatching. ICT tools have considerably influenced health care in the last decade, but in many cases not as far as it was expected by Haux et al. in 2002. In most cases this is not a matter of the availability of technical solutions but of organizational and ethical issues. We need innovative and modern information system architectures which support multiple use of data for patient care as well as for research and reporting and which are able to integrate data from home monitoring into a patient centered health record. Since innovative technology is available the efficient and wide-spread use in health care has to be enabled by systematic information management.

Past and next 10 years of medical informatics.

More than 10 years ago Haux et al. tried to answer the question how health care provision will look like in the year 2013. A follow-up workshop was held in Braunschweig, Germany, for 2 days in May, 2013, with 20 invited international experts in biomedical and health informatics. Among other things it had the objectives to discuss the suggested goals and measures of 2002 and how priorities on MI research in this context should be set from the viewpoint of today. The goals from 2002 are now as up-to-date as they were then. The experts stated that the three goals: "patient-centred recording and use of medical data for cooperative care"; "process-integrated decision support through current medical knowledge" and "comprehensive use of patient data for research and health care reporting" have not been reached yet and are still relevant. A new goal for ICT in health care should be the support of patient centred personalized (individual) medicine. MI as an academic discipline carries out research concerning tools that support health care professionals in their work. This research should be carried out without the pressure that it should lead to systems that are immediately and directly accepted in practice.

Evaluating REDCap as the Central Data Collection Tool for the Hamburg City Health Study.

INTRODUCTION: The collection of examination data for large clinical studies is often done with proprietary systems, which are accompanied by several disadvantages such as high cost and low flexibility. With the use of open-source tools, these disadvantages can be overcome and thereby improve data collection as well as data quality. Here we exemplary use the data collection process of the Hamburg City Health Study (HCHS), carried out at the University Medical Center Hamburg-Eppendorf (UKE). We evaluated how the recording of the examination data can be converted from an established, proprietary electronic healthcare record (EHR) system to the free-to-use Research Electronic Data Capture (REDCap) software. METHODS: For this purpose, a technical conversion of the EHR system is described first. Metafiles derived from the EHR system were used for REDCap electronic case report form (eCRF) building. The REDCap system was tested by HCHS study assistants via completion of self-developed tasks mimicking their everyday study life. Usability was quantitatively evaluated via the IBM Computer System Usability Questionnaire (CSUQ) and qualitatively assessed with a semi-structured interview. RESULTS: With the IBM CSUQ, the study assistants rated the usage of the basic REDCap system for HCHS examination data collection with an overall score of 4.39, which represents a medium acceptance. The interview feedback was used to formulate user stories to subsequently increase the administrative sovereignty and to conceptualize a REDCap HCHS information technology (IT) infrastructure. CONCLUSION: Our work aims to serve as a template for evaluating the feasibility of a conversion from a proprietary to a free-to-use data collection tool for large clinical studies such as the HCHS. REDCap has great potential, but extensions and an integration to the current IT infrastructure are required.

Mapping the Oncological Basis Dataset to the Standardized Vocabularies of a Common Data Model: A Feasibility Study.

In their joint effort against cancer, all involved parties within the German healthcare system are obligated to report diagnostics, treatments, progression, and follow-up information for tumor patients to the respective cancer registries. Given the federal structure of Germany, the oncological basis dataset (oBDS) operates as the legally required national standard for oncological reporting. Unfortunately, the usage of various documentation software solutions leads to semantic and technical heterogeneity of the data, complicating the establishment of research networks and collective data analysis. Within this feasibility study, we evaluated the transferability of all oBDS characteristics to the standardized vocabularies, a metadata repository of the observational medical outcomes partnership (OMOP) common data model (CDM). A total of 17,844 oBDS expressions were mapped automatically or manually to standardized concepts of the OMOP CDM. In a second step, we converted real patient data retrieved from the Hamburg Cancer Registry to the new terminologies. Given our pipeline, we transformed 1773.373 cancer-related data elements to the OMOP CDM. The mapping of the oBDS to the standardized vocabularies of the OMOP CDM promotes the semantic interoperability of oncological data in Germany. Moreover, it allows the participation in network studies of the observational health data sciences and informatics under the usage of federated analysis beyond the level of individual countries.

A Unified Data Architecture for Assessing Motor Symptoms in Parkinson's Disease.

INTRODUCTION: The diagnosis and treatment of Parkinson's disease depend on the assessment of motor symptoms. Wearables and machine learning algorithms have emerged to collect large amounts of data and potentially support clinicians in clinical and ambulant settings. STATE OF THE ART: However, a systematical and reusable data architecture for storage, processing, and analysis of inertial sensor data is not available. Consequently, datasets vary significantly between studies and prevent comparability. CONCEPT: To simplify research on the neurodegenerative disorder, we propose an efficient and real-time-optimized architecture compatible with HL7 FHIR backed by a relational database schema. LESSONS LEARNED: We can verify the adequate performance of the system on an experimental benchmark and in a clinical experiment. However, existing standards need to be further optimized to be fully sufficient for data with high temporal resolution.

Pragmatic screening for heart failure in the general population using an electrocardiogram-based neural network.

AIMS: We aim to develop a pragmatic screening tool for heart failure at the general population level. METHODS AND RESULTS: This study was conducted within the Hamburg-City-Health-Study, an ongoing, prospective, observational study enrolling randomly selected inhabitants of the city of Hamburg aged 45-75 years. Heart failure was diagnosed per current guidelines. Using only digital electrocardiograms (ECGs), a convolutional neural network (CNN) was built to discriminate participants with and without heart failure. As comparisons, known risk variables for heart failure were fitted into a logistic regression model and a random forest classifier. Of the 5299 individuals included into this study, 318 individuals (6.0%) had heart failure. Using only the digital ECGs instead of several risk variables as an input, the CNN provided a comparable predictive accuracy for heart failure versus the logistic regression model and the random forest classifier [area under the curve (AUC) of 0.75, a sensitivity of 0.67 and a specificity of 0.69 for the CNN; AUC 0.77, a sensitivity of 0.63 and a specificity of 0.76 for the logistic regression; AUC 0.79, a sensitivity of 0.67 and a specificity of 0.72 for the random forest classifier]. CONCLUSIONS: Using a CNN build on digital ECGs only and requiring no additional input, we derived a screening tool for heart failure in the general population. This could be perfectly embedded into clinical routine of general practitioners, as it builds on an already established diagnostic tool and does not require additional, time-consuming input. This could help to alleviate the underdiagnosis of heart failure.

IT behind a platform for Translational Cancer Research - concept and objectives.

The German Consortium for Translational Cancer Research (DKTK) and the Rhine-Main Translational Cancer Research Network (RM-TCRN) are designed to exploit large population cohorts of cancer patients for the purpose of bio-banking, clinical trials, and clinical cancer registration. Hence, the success of these platforms is heavily dependent on the close interlinking of clinical data from cancer patients, information from study registries, and data from bio-banking systems of different laboratories and scientific institutions. This article referring to the poster discusses the main challenges of the platforms from an information technology point of view, legal and data security issues, and outlines an integrative IT-concept concerning a decentralized, distributed search approach where data management and search is in compliance with existing legislative rules.

Surgical data science - from concepts toward clinical translation.

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.

Virtual ward round.

"Virtual Ward Round" is a web-based blended learning tool. The program simulates hospital ward rounds. Within a virtual environment, students make diagnosis and order treatments. Tutors prepare cases easily to ensure realistic cases directly linked to the corresponding classes. The program "Virtual Ward Round" will hopefully be enrichment to the curriculum-based teaching.

Technical data evaluation of a palliative care web-based documentation system.

A technical analysis of the web-based patient documentation system, eKernPäP, was conducted. The system is used by interdisciplinary pediatric palliative care teams in Germany to document outpatient care. The data of the system and the data of an external web analytic system have been evaluated. The results gave an overview how the system is used and what information is generated. A detailed analysis of singular forms showed that not all forms were filled in completely. With the help of the external web analytic system the navigation behavior of the users could be retraced. The users followed the given navigation from top to bottom. An existing exception in this pattern turned out to be misplacement and will be corrected in the next version. The technical analysis proved to be a good tool for improving a web-based documentation system.

Providing trust and interoperability to federate distributed biobanks.

Biomedical research requires large numbers of well annotated, quality-assessed samples which often cannot be provided by a single biobank. Connecting biobanks, researchers and service providers raises numerous challenges including trust among partners and towards the infrastructure as well as interoperability problems. Therefore we develop a holistic, open-source and easy-to-use IT infrastructure. Our federated approach allows partners to reflect their organizational structures and protect their data sovereignty. The search service and the contact arrangement processes increase data sovereignty without stigmatizing for rejecting a specific cooperation. The infrastructure supports daily processes with an integrated basic sample manager and user-definable electronic case report forms. Interfaces for existing IT systems avoid re-entering of data. Moreover, resource virtualization is supported to make underutilized resources of some partners accessible to those with insufficient equipment for mutual benefit. The functionality of the resulting infrastructure is outlined in a use-case to demonstrate collaboration within a translational research network. Compared to other existing or upcoming infrastructures, our approach has ultimately the same goals, but relies on gentle incentives rather than top-down imposed progress.