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Multimodal Computing & Machine Intelligence (MCMI)

The MCMI research group headed by Prof. Dr. Christin Seifert focuses on the transfer of fundamental research in Machine Learning, Information Extraction, Natural Language Processing and Semantic Technologies and its application to the medical domain, and to Oncology in particular.

 

The main research areas of the group are: Machine Learning on Multimodal Data, Knowledge Extraction, and Explainable AI.

 

In Machine Learning we address questions relevant, but not limited to, clinical decision support. Examples include predictive modelling, survival analysis, causal discovery, and subgroup identification.

 

Knowledge Extraction is the essential building block for obtaining relevant, harmonized information from patient data (e.g. medication or co-morbidity history from electronic health records). This information serves as input for machine-learning based analysis and predictions which in turn is the basis to support clinical decisions.

 

Explainable AI (X-AI) is a young research field addressing the challenge of providing an assurance that the artificial intelligence “does the right thing for the right reasons” which is key to enabling the uptake of artificial intelligence in high-stakes decision scenarios, such as health-care. X-AI envisions efficient human-machine cooperation by developing intelligible automated decision support agents. These intelligible extraction and prediction processes help fostering the stakeholders’ acceptance and trust.

The MCMI research group is part of the Cancer Research Center Cologne Essen (CCCE), the Initiative Network of Excellence in Cancer Medicine NRW.

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Institut für künstliche Intelligenz
in der Medizin
Universitätsklinikum Essen
Girardetstraße 2, 45131 Essen
AI Futurelab Logo
Institut für künstliche Intelligenz
in der Medizin
Universitätsklinikum Essen
Girardetstraße 2, 45131 Essen
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