Medical Applications
Engineering Answers, Not Black Boxes
Anomaly Detection
Machine learning plays a crucial role in identifying anomalies in medicine by leveraging its ability to analyze large datasets and detect patterns that may be challenging for human experts to recognize.
DIM-GP models can analyze electronic health records (EHRs) and other clinical data to identify unusual patterns in patient history, medication usage, or diagnostic test results.
Gradient based Sensivity
Use DIM-GP to determine critical factors
Sensitivity analysis is a technique used in various medical applications to assess the impact of changes or uncertainties in input parameters on the outcomes of models or simulations. This analysis helps to understand the robustness and reliability of models, making it a valuable tool in medical research and decision-making.
References in Healthcare
Joined Research Project EPWUF-KI
Relieving the burden of care in the area of wound treatment using the example of diabetic foot syndrome through a hybrid AI system
https://www.interaktive-technologien.de/projekte/epwufki
Joint Research Project SmartInjury
Smart Injury Prevention – Development of intelligent software for personalized prediction of running injuries through novel Machine learning methods
https://www.iws-nord.de/referenzen/smart-injury-prevention
Multi Fidelity
Multi-fidelity is the utilization of models with varying levels of accuracy and computational expense. STOCHOS© only deploys high-fidelity features, that is more accurate, and computationally expensive modelling in those areas where the model uncertainty is high. By then automatically combining the information from areas with different fidelity levels into a single model, we can achieve the same accuracy as in a fully high fidelity model in only a fraction of time. In combination with our integrated noise detection this allows real data modelling of highly complex problems even on GPU or CPU with astonishing accuracy.
Sensivity Analysis
Global Sensivity
Local Sensivity
Always get immediate sensitivity analysis for your models to understand the weight of different parameters
STOCHOS brings a new level of transparency to machine learning by combining probabilistic modeling with automatic sensitivity analysis. It helps you understand not just what your model predicts, but why. With built-in tools for both global and local parameter sensitivity, you can easily explore how different inputs influence your results. This makes it easier to identify key drivers, assess model robustness, and support more informed decision-making—all without additional coding or complex setup.
high-accuracy engineering predictions –
fast, reliable, and built for real-world complexity.
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