AI-assisted decision support for hematology.
The problem
The project
Researchers and physicians are constantly searching for new treatment options for patients with rare blood cancers such as myelodysplastic syndrome (MDS), acute myeloid leukemia (AML) or multiple myeloma.
To find the next best treatment option for their patients, they use, among other things, research publications. However, the volume of research publications and the frequency with which they are published pose a challenge for practicing hematologists. They are faced with the task of finding the right publications for their patients in the huge amount of data in order to make individual therapy decisions.
The greatest difficulty is matching individual patient:s data such as demographics, previous therapies, and health conditions with natural language descriptions from clinical trials and research.
The solution
Our contribution
The NLP solution we have developed is designed to help hematologists find the right publications for their patients and thus support physicians in making individual therapy decisions.
Here, clinical publications are selected and ranked according to their relevance to a specific medical case. Combining the human intelligence of hematologists with artificial intelligence (AI) will guide the diagnostic process and the development of personalized treatment decisions, making cancer care more patient-centered, targeted and effective.
The NLP component is being developed in close collaboration with the Clinic for Hematology, Cell Therapy and Hemostaseology of the University Hospital Leipzig and the Innovation Center Computer Assisted Surgery ICCAS and will integrate the KAIT platform as another component. In addition to the integration of the AI-based publication search, the platform also includes current clinical guidelines and enables case comparisons.
Our result
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View more projects
Contact us
- Critical and holistic evaluation of the approach
- Development of guidance for reliable implementation
- Free of charge and without obligation