Use Cases
Hier stellen wir potenzielle Use Cases vor, die mithilfe der CAIMed-Forschung realisiert werden können. Die Anwendungsbeispiele veranschaulichen, wie die im Projekt entwickelten Methoden und Erkenntnisse in der Praxis eingesetzt werden könnten. Die kurzen Beschreibungen erläutern die zugrunde liegenden Ideen, zentralen Fragestellungen und technische Hintergründe, um einen Eindruck zu vermitteln, welche Potenziale die CAIMed-Forschung bietet.
By enabling the development of accurate, privacy-preserving models from distributed datasets, FEDCOV allows healthcare providers to collaboratively build predictive models for COVID-19 severity, long-term symptoms, and treatment outcomes without sharing sensitive patient data. This approach ensures that patient information remains secure, while still enabling the creation of robust models that can improve clinical decision-making and personalized treatment plans. Additionally, the project addresses fairness and interpretability, ensuring that the models are unbiased and transparent, which is crucial for equitable healthcare delivery across diverse patient populations.
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The prevalence of PAIS remains poorly understood, highlighting the need for deeper insights into its biological mechanisms. AID-PAIS will analyse diverse data types, identify molecular signatures, and uncover causal relationships driving persistent symptoms. AID-PAIS aims to enhance diagnostic accuracy, enable personalized treatments, and reduce misdiagnoses, ultimately improving public health strategies, lowering long-term healthcare costs, and strengthening preparedness for future pandemics.
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AI & Causality
By applying AI-driven Electrical Impedance Tomography (EIT), researchers can reconstruct high-resolution lung images from minimal electrical data. This enables early detection of subtle lung damage and real-time bedside monitoring. Because it’s non-invasive and cost-effective, changes in lung function can be promptly managed, potentially reducing complications and speeding recovery.
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Digital Twins
Based on the dataset from the ELISE project, a rule-based labeling model was created, on the basis of which a prediction model is being developed that can predict sepsis 6 to 12 hours before the onset of the disease. This model should enable early therapy and help to prevent the negative effects of sepsis on the course of critically ill children.
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To analyze and address MODS (multi-organ dysfunction syndrome) in pediatric intensive care patients, a model is being developed to process and analyze data from a complex pediatric intensive care dataset. This model aims to identify causal relationships in the progression of MODS, enabling better understanding and prediction of organ dysfunction.
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