A great challenge in the use of standardized cancer registry data is deriving reliable, evidence-based results from large amounts of data. A solution could be its mapping to a common data model such as OMOP, which represents knowledge in a unified semantic base, enabling decentralized analysis. The recently released Episode Domain of the OMOP CDM allows episodic modelling of a patient’ disease and treatment phases. In this study, we mapped oncology registry data to the Episode Domain. A total of 184,718 Episodes could be implemented, with the Concept of Cancer Drug Treatment most frequently. Additionally, source data were mapped to new terminologies as part of the release. It was possible to map ≈ 73.8% of the source data to the respective OMOP standard. Best mapping was achieved in the Procedure Domain with 98.7%. To evaluate the implementation, the survival probabilities of the CDM and source system were calculated (n = 2756/2902, median OAS = 82.2/91.1 months, 95% Cl = 77.4–89.5/84.4–100.9). In conclusion, the new release of the CDM increased its applicability, especially in observational cancer research. Regarding the mapping, a higher score could be achieved if terminologies which are frequently used in Europe are included in the Standardized Vocabulary Metadata Repository.

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.

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.

In Molecular Tumor Boards, a team of experts discuss the individual therapy options of a cancer patient based on their individual molecular profile. The process—from recommendation request, through molecular diagnosis, to a personalized therapy recommendation—is complex and time-consuming. Therefore, process optimization is needed to decrease the workload of physicians and to standardize the process. For this purpose, we modeled the current workflow of the Molecular Tumor Board at the University Hospital Hamburg-Eppendorf on Service-Oriented Architecture using Business Process Modeling and Notation to highlight areas for improvement. This identified many manual tasks and an extensive workload for the physician. We then created a novel, simplified, more efficient workflow in which the physician is supported by additional software. In summary, we show that the use of Service-Oriented Architecture using Business Process Modeling and Notation for Molecular Tumor Board processes promotes rapid adaptability, standardization, interoperability, quality assurance, and facilitates collaboration.

In the realm of medical research, preserving patient privacy while facilitating effective research and collaborations poses a significant challenge. Data protection impact assessments (DPIAs) and associated methodologies have emerged as a response to this dual imperative. DPIAs necessitate expertise across diverse domains, resulting in a complex procedural landscape. To address this, we present “DPIA click&go”, a user-friendly tool designed to streamline the DPIA process in a plug-and-play manner. This tool enables users to semi-automatically select risks from predefined categories, construct evaluation matrices, access risk-mitigating measures, and re-evaluate risks after the application of mitigation strategies. Tailoring risks and measures to each institute’s needs is essential, facilitated by the provided data model, considerably simplifying DPIA creation at an institutional level. The efficacy of the DPIA click&go tool was validated with a real-world project, comparing its performance with a manually created DPIA in terms of risk coverage and mitigation strategies. The promising outcomes of this tool underscore its potential within the national data protection landscape, suggesting its possible foundational role in harmonizing data protection practices at a larger, potentially, European or global, scale.

During the in-hospital optimization of dopaminergic dosage for Parkinson’s disease, drug-induced dyskinesias emerge as a common side effect. Wrist-worn devices present a substantial opportunity for continuous movement recording and the supportive identification of these dyskinesias. To bridge the gap between dyskinesia assessment and machine learning-enabled detection, the recorded information requires meaningful data representations. This study evaluates and compares two distinct representations of sensor data: a task-dependent, semantically grounded approach and automatically extracted large-scale time-series features. Each representation was assessed on public datasets to identify the best-performing machine learning model and subsequently applied to our own collected dataset to assess generalizability. Data representations incorporating semantic knowledge demonstrated comparable or superior performance to reported works, with peak F₁ scores of 0.68. Generalization to our own dataset from clinical practice resulted in an observed F₁ score of 0.53 using both setups. These results highlight the potential of semantic movement data analysis for dyskinesia detection. Dimensionality reduction in accelerometer-based movement data positively impacts performance, and models trained with semantically obtained features avoid overfitting. Expanding cohorts with standardized neurological assessments labeled by medical experts is essential for further improvements.