Controller-Synthese für Services mit Daten

Die steigende Nachfrage an immer komplexeren Systemen in verschiedensten wirtschaftlichen Bereichen, erfordert Strategien, die Wartbarkeit und Wiederverwendbarkeit unterstützen. An diesem Punkt setzen service-orientierte Architekturen (SOAn) an. Dieses Paradigma fordert die Aufspaltung von Funktionalität in Services, die komponiert werden können, um eine gewünschte, komplexe Funktionalität zu erreichen. Besonders in sicherheitskritischen Bereichen, kann eine fehlerbehaftete Komposition jedoch zu hohen finanziellen Einbußen oder sogar zu lebensbedrohlichen Situationen führen. Um die Korrektheit sicherzustellen, müssen Kompositionsmethoden im Vorfeld definierte Eigenschaften garantieren und die, durch die unabhängige Entwicklung auftretenden, Interface-Inkompatibilitäten behandeln. Existierende Ansätze zur automatisierten Service-Komposition durch Controller-Synthese beinhalten jedoch keine formale Datenbehandlung und können daher nicht mit datenabhängigem Verhalten umgehen. In der vorliegenden Arbeit, löse ich dieses Problem durch die Bereitstellung eines Ansatzes zur automatisierten Synthese datenabhängiger, korrekter Service-Controller. Dabei wird ein Controller direkt aus den spezifizierten Anforderungen und dem Verhalten der Services erzeugt. Basierend auf den Annahmen, dass die Anforderungen in RCTL, einer Untermenge der Computational Tree Logic (CTL), spezifiziert und die Services als Algebraische Petrinetze (APNe) gegeben sind, vereinigt mein neuartiger Ansatz die beiden Formalismen und unterstützt eine zuverlässige Extraktion des Controller-Verhaltens. Durch die Nutzung der APNe, erlaubt der Ansatz eine formale Datenbehandlung und somit eine Betrachtung datenabhängigen Verhaltens. Die Anwendbarkeit meines Ansatzes habe ich an drei Fallstudien aus dem medizinischen Bereich gezeigt, wo Geräte sicher miteinander kommunizieren müssen.The continuously increasing demand for more complex systems in various economical domains requires a strategy that supports maintainability and reusability. This is addressed by the service-oriented architecture (SOA)}-paradigm that encourages the encapsulation of functionality into services. To achieve a specific functionality, services can be composed. Especially in safety-critical systems, an incorrect composition of various components can lead to high financial losses or even life threatening situations. To ensure the correctness, composition methods must particularly be able to guarantee pre-specified requirements and to overcome interface incompatibilities, which result from the independent development of the single services. However, current approaches for automated service composition via controller synthesis do not support a formal data-treatment and do not cope with data-dependent behavior. In this thesis, we overcome this problem by providing an approach for the automated synthesis of data-dependent service controllers that are correct-by-construction. The core idea is to synthesize such a controller directly from given requirements and the behavior of the services. Based on the assumptions that the requirements are specified using a subset of Computational Tree Logic (CTL), called RCTL, and that the services are given as algebraic Petri Nets (APNs), our novel synthesis process unifies the two formalisms and enables a reliable extraction of the controller behavior. Especially due to the use of APNs, our approach supports a formal data-treatment and enables a consideration of data-dependent behavior. With our synthesis process, which is based on a successive combination of requirements and services, we provide a practical applicable approach that works fully automatically. We show the applicability of our approach using three case studies in which medical devices interact with each other

Conceptual design of a generic data harmonization process for OMOP common data model

Abstract Background To gain insight into the real-life care of patients in the healthcare system, data from hospital information systems and insurance systems are required. Consequently, linking clinical data with claims data is necessary. To ensure their syntactic and semantic interoperability, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) from the Observational Health Data Sciences and Informatics (OHDSI) community was chosen. However, there is no detailed guide that would allow researchers to follow a generic process for data harmonization, i.e. the transformation of local source data into the standardized OMOP CDM format. Thus, the aim of this paper is to conceptualize a generic data harmonization process for OMOP CDM. Methods For this purpose, we conducted a literature review focusing on publications that address the harmonization of clinical or claims data in OMOP CDM. Subsequently, the process steps used and their chronological order as well as applied OHDSI tools were extracted for each included publication. The results were then compared to derive a generic sequence of the process steps. Results From 23 publications included, a generic data harmonization process for OMOP CDM was conceptualized, consisting of nine process steps: dataset specification, data profiling, vocabulary identification, coverage analysis of vocabularies, semantic mapping, structural mapping, extract-transform-load-process, qualitative and quantitative data quality analysis. Furthermore, we identified seven OHDSI tools which supported five of the process steps. Conclusions The generic data harmonization process can be used as a step-by-step guide to assist other researchers in harmonizing source data in OMOP CDM

Use of Metadata-Driven Approaches for Data Harmonization in the Medical Domain: Scoping Review

BackgroundMultisite clinical studies are increasingly using real-world data to gain real-world evidence. However, due to the heterogeneity of source data, it is difficult to analyze such data in a unified way across clinics. Therefore, the implementation of Extract-Transform-Load (ETL) or Extract-Load-Transform (ELT) processes for harmonizing local health data is necessary, in order to guarantee the data quality for research. However, the development of such processes is time-consuming and unsustainable. A promising way to ease this is the generalization of ETL/ELT processes. ObjectiveIn this work, we investigate existing possibilities for the development of generic ETL/ELT processes. Particularly, we focus on approaches with low development complexity by using descriptive metadata and structural metadata. MethodsWe conducted a literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We used 4 publication databases (ie, PubMed, IEEE Explore, Web of Science, and Biomed Center) to search for relevant publications from 2012 to 2022. The PRISMA flow was then visualized using an R-based tool (Evidence Synthesis Hackathon). All relevant contents of the publications were extracted into a spreadsheet for further analysis and visualization. ResultsRegarding the PRISMA guidelines, we included 33 publications in this literature review. All included publications were categorized into 7 different focus groups (ie, medicine, data warehouse, big data, industry, geoinformatics, archaeology, and military). Based on the extracted data, ontology-based and rule-based approaches were the 2 most used approaches in different thematic categories. Different approaches and tools were chosen to achieve different purposes within the use cases. ConclusionsOur literature review shows that using metadata-driven (MDD) approaches to develop an ETL/ELT process can serve different purposes in different thematic categories. The results show that it is promising to implement an ETL/ELT process by applying MDD approach to automate the data transformation from Fast Healthcare Interoperability Resources to Observational Medical Outcomes Partnership Common Data Model. However, the determining of an appropriate MDD approach and tool to implement such an ETL/ELT process remains a challenge. This is due to the lack of comprehensive insight into the characterizations of the MDD approaches presented in this study. Therefore, our next step is to evaluate the MDD approaches presented in this study and to determine the most appropriate MDD approaches and the way to integrate them into the ETL/ELT process. This could verify the ability of using MDD approaches to generalize the ETL process for harmonizing medical data

Opportunities of Digital Infrastructures for Disease Management—Exemplified on COVID-19-Related Change in Diagnosis Counts for Diabetes-Related Eye Diseases

Background: Retrospective research on real-world data provides the ability to gain evidence on specific topics especially when running across different sites in research networks. Those research networks have become increasingly relevant in recent years; not least due to the special situation caused by the COVID-19 pandemic. An important requirement for those networks is the data harmonization by ensuring the semantic interoperability. Aims: In this paper we demonstrate (1) how to facilitate digital infrastructures to run a retrospective study in a research network spread across university and non-university hospital sites; and (2) to answer a medical question on COVID-19 related change in diagnostic counts for diabetes-related eye diseases. Materials and methods: The study is retrospective and non-interventional and runs on medical case data documented in routine care at the participating sites. The technical infrastructure consists of the OMOP CDM and other OHDSI tools that is provided in a transferable format. An ETL process to transfer and harmonize the data to the OMOP CDM has been utilized. Cohort definitions for each year in observation have been created centrally and applied locally against medical case data of all participating sites and analyzed with descriptive statistics. Results: The analyses showed an expectable drop of the total number of diagnoses and the diagnoses for diabetes in general; whereas the number of diagnoses for diabetes-related eye diseases surprisingly decreased stronger compared to non-eye diseases. Differences in relative changes of diagnoses counts between sites show an urgent need to process multi-centric studies rather than single-site studies to reduce bias in the data. Conclusions: This study has demonstrated the ability to utilize an existing portable and standardized infrastructure and ETL process from a university hospital setting and transfer it to non-university sites. From a medical perspective further activity is needed to evaluate data quality of the utilized real-world data documented in routine care and to investigate its eligibility of this data for research