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PublicationPOS0881 Specific AI-Generated Pattern of Tender Joints and Tenderness at Enthesial Sites are Predictive for Objective Detection of Musculoskeletal Inflammation in Psoriasis Patients( 2023-05-30)
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PublicationA machine learning method for the identification and characterization of novel COVID-19 drug targets( 2023-05-03)
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PublicationSymptom diaries as a digital tool to detect SARS-CoV-2 infections and differentiate between prevalent variants( 2022-11-14)The COVID-19 pandemic and the high numbers of infected individuals pose major challenges for public health departments. To overcome these challenges, the health department in Cologne has developed a software called DiKoMa. This software offers the possibility to track contact and index persons, but also provides a digital symptom diary. In this work, the question of whether these can also be used for diagnostic purposes will be investigated. Machine learning makes it possible to identify infections based on early symptom profiles and to distinguish between the predominant dominant variants. Focusing on the occurrence of the symptoms in the first week, a decision tree is trained for the differentiation between contact and index persons and the prevailing dominant variants (Wildtype, Alpha, Delta, and Omicron). The model is evaluated, using sex- and age-stratified cross-validation and validated by symptom profiles of the first 6 days. The variants achieve an AUC-ROC from 0.89 for Omicron and 0.6 for Alpha. No significant differences are observed for the results of the validation set (Alpha 0.63 and Omicron 0.87). The evaluation of symptom combinations using artificial intelligence can determine the individual risk for the presence of a COVID-19 infection, allows assignment to virus variants, and can contribute to the management of epidemics and pandemics on a national and international level. It can help to reduce the number of specific tests in times of low labor capacity and could help to early identify new virus variants.
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PublicationOmics and Multi-Omics Analysis for the Early Identification and Improved Outcome of Patients with Psoriatic Arthritis( 2022-09-24)The definitive diagnosis and early treatment of many immune-mediated inflammatory diseases (IMIDs) is hindered by variable and overlapping clinical manifestations. Psoriatic arthritis (PsA), which develops in ~30% of people with psoriasis, is a key example. This mixed-pattern IMID is apparent in entheseal and synovial musculoskeletal structures, but a definitive diagnosis often can only be made by clinical experts or when an extensive progressive disease state is apparent. As with other IMIDs, the detection of multimodal molecular biomarkers offers some hope for the early diagnosis of PsA and the initiation of effective management and treatment strategies. However, specific biomarkers are not yet available for PsA. The assessment of new markers by genomic and epigenomic profiling, or the analysis of blood and synovial fluid/tissue samples using proteomics, metabolomics and lipidomics, provides hope that complex molecular biomarker profiles could be developed to diagnose PsA. Importantly, the integration of these markers with high-throughput histology, imaging and standardized clinical assessment data provides an important opportunity to develop molecular profiles that could improve the diagnosis of PsA, predict its occurrence in cohorts of individuals with psoriasis, differentiate PsA from other IMIDs, and improve therapeutic responses. In this review, we consider the technologies that are currently deployed in the EU IMI2 project HIPPOCRATES to define biomarker profiles specific for PsA and discuss the advantages of combining multi-omics data to improve the outcome of PsA patients.
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PublicationDie zeitlich-räumliche Verteilung von COVID-19 in Köln und beeinflussende soziale Faktoren im Zeitraum Februar 2020 bis Oktober 2021( 2022)Hintergrund und Ziele: Schon in der frühen Phase der global sehr verschieden verlaufenden COVID-19-Pandemie zeigten sich Hinweise auf den Einfluss sozioökonomischer Faktoren auf die Ausbreitungsdynamik der Erkrankung, die vor allem ab der zweiten Phase (September 2020) Menschen mit geringerem sozioökonomischen Status stärker betraf. Solche Effekte können sich auch innerhalb einer Großstadt zeigen. Die vorliegende Studie visualisiert und untersucht die zeitlich-räumliche Verbreitung aller in Köln gemeldeten COVID-19-Fälle (Februar 2020–Oktober 2021) auf Stadtteilebene und deren mögliche Assoziation mit sozioökonomischen Faktoren. Methoden: Pseudonymisierte Daten aller in Köln gemeldeten COVID-19-Fälle wurden geocodiert, deren Verteilung altersstandardisiert auf Stadtteilebene über 4 Zeiträume kartiert und mit der Verteilung von sozialen Faktoren verglichen. Der mögliche Einfluss der ausgewählten Faktoren wird zudem in einer Regressionsanalyse in einem Modell mit Fallzuwachsraten betrachtet. Ergebnisse: Das kleinräumige lokale Infektionsgeschehen ändert sich im Pandemieverlauf. Stadtteile mit schwächeren sozioökonomischen Indizes weisen über einen großen Teil des pandemischen Verlaufs höhere Inzidenzzahlen auf, wobei eine positive Korrelation zwischen den Armutsrisikofaktoren und der altersstandardisierten Inzidenz besteht. Die Stärke dieser Korrelation ändert sich im zeitlichen Verlauf. Schlussfolgerung: Die zeitnahe Beobachtung und Analyse der lokalen Ausbreitungsdynamik lassen auch auf der Ebene einer Großstadt die positive Korrelation von nachteiligen sozioökonomischen Faktoren auf die Inzidenzrate von COVID-19 erkennen und können dazu beitragen, lokale Eindämmungsmaßnahmen zielgerecht zu steuern.
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PublicationAdvanced Sensing and Human Activity Recognition in Early Intervention and Rehabilitation of Elderly People( 2020)Ageing is associated with a decline in physical activity and a decrease in the ability to perform activities of daily living, affecting physical and mental health. Elderly people or patients could be supported by a human activity recognition (HAR) system that monitors their activity patterns and intervenes in case of change in behavior or a critical event has occurred. A HAR system could enable these people to have a more independent life. In our approach, we apply machine learning methods from the field of human activity recognition (HAR) to detect human activities. These algorithmic methods need a large database with structured datasets that contain human activities. Compared to existing data recording procedures for creating HAR datasets, we present a novel approach, since our target group comprises of elderly and diseased people, who do not possess the same physical condition as young and healthy persons. Since our targeted HAR system aims at supporting elderly and diseased people, we focus on daily activities, especially those to which clinical relevance in attributed, like hygiene activities, nutritional activities or lying positions. Therefore, we propose a methodology for capturing data with elderly and diseased people within a hospital under realistic conditions using wearable and ambient sensors. We describe how this approach is first tested with healthy people in a laboratory environment and then transferred to elderly people and patients in a hospital environment. We also describe the implementation of an activity recognition chain (ARC) that is commonly used to analyse human activity data by means of machine learning methods and aims to detect activity patterns. Finally, the results obtained so far are presented and discussed as well as remaining problems that should be addressed in future research.
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PublicationA review of machine learning for the optimization of production processes( 2019)Due to the advances in the digitalization process of the manufacturing industry and the resulting available data, there is tremendous progress and large interest in integrating machine learning and optimization methods on the shop floor in order to improve production processes. Additionally, a shortage of resources leads to increasing acceptance of new approaches, such as machine learning to save energy, time, and resources, and avoid waste. After describing possible occurring data types in the manufacturing world, this study covers the majority of relevant literature from 2008 to 2018 dealing with machine learning and optimization approaches for product quality or process improvement in the manufacturing industry. The review shows that there is hardly any correlation between the used data, the amount of data, the machine learning algorithms, the used optimizers, and the respective problem from the production. The detailed correlations between these criteria and the recent progress made in this area as well as the issues that are still unsolved are discussed in this paper.
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PublicationKünstliche Intelligenz und die Potenziale des maschinellen Lernens für die Industrie( 2017)Maschinelles Lernen ist die Schlüsseltechnologie für intelligente Systeme. Beson¬ders erfolgreich ist in den letzten Jahren das Lernen tiefer Modelle aus großen Datenmengen - ""Deep Learning"". Mit dem Internet der Dinge rollt die nächste, noch größere Datenwelle auf uns zu. Hier bietet die Künstliche Intelligenz beson¬dere Chancen für die deutsche Industrie, wenn sie schnell genug in die Digitalisierung einsteigt.
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PublicationE2mC: Improving Emergency Management Service Practice through Social Media and Crowdsourcing Analysis in Near Real Time( 2017)In the first hours of a disaster, up-to-date information about the area of interest is crucial for effective disaster management. However, due to the delay induced by collecting and analysing satellite imagery, disaster management systems like the Copernicus Emergency Management Service (EMS) are currently not able to provide information products until up to 48-72 h after a disaster event has occurred. While satellite imagery is still a valuable source for disaster management, information products can be improved through complementing them with user-generated data like social media posts or crowdsourced data. The advantage of these new kinds of data is that they are continuously produced in a timely fashion because users actively participate throughout an event and share related information. The research project Evolution of Emergency Copernicus services (E2mC) aims to integrate these novel data into a new EMS service component called Witness, which is presented in this paper. Like this, the timeliness and accuracy of geospatial information products provided to civil protection authorities can be improved through leveraging user-generated data. This paper sketches the developed system architecture, describes applicable scenarios and presents several preliminary case studies, providing evidence that the scientific and operational goals have been achieved.
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PublicationBig Data in Medizin und Gesundheitswesen( 2015)Das Gesundheitswesen ist eine der Branchen mit dem größten Potenzial für Big Data. Laut der üblichen Definition bezieht sich Big Data auf die Tatsache, dass Datenmengen mittlerweile oft zu groß und zu heterogen sind und zu schnell wachsen, um sie mit herkömmlichen Technologien zu speichern, zu analysieren und nutzbar zu machen. Vorangetrieben wird Big Data durch drei technologische Trends: Geschäftsprozesse werden vermehrt elektronisch durchgeführt, Privatpersonen produzieren immer mehr Daten - z. B. in sozialen Netzwerken - und die Digitalisierung schreitet immer weiter voran, durch Smartphones und Apps bis in den Alltag. Auch in Medizin und Gesundheitswesen zeichnen sich neue Trends zu interessanten neuen Datenquellen und zu innovativen Möglichkeiten der Datenanalyse ab. Dies betrifft zum einen die Forschung. Hier benötigen etwa die Omics-Forschung klar Big-Data-Technologien. In der medizinischen Praxis bieten insbesondere die elektronische Patientenakte, freie öffentliche Daten und der Trend des Quantified Self, also der Vermessung des eigenen Verhaltens, neue Möglichkeiten für die Datenanalyse. Hinsichtlich der Analytik gibt es in der jüngsten Vergangenheit insbesondere deutliche Fortschritte bei der Informationsextraktion aus Textdaten, die viele Daten aus der medizinischen Dokumentation für eine Analyse erschließt. Gleichzeitig ist aber hier durch spezielle fachliche, rechtliche und ethische Rahmenbedingungen in Medizin und Gesundheitswesen die Anwendung von Big Data noch deutlich weniger ausgeprägt als in anderen Branchen. Erste interessante Best-Practice-Beispiele in der Medizin und im Gesundheitsbereich lassen aber innovative Ansätze und Ergebnisse erwarten. Der vorliegende Beitrag gibt eine Übersicht über die Potenziale von Big Data in der Medizin und im Gesundheitswesen.
