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Data from historical patients provides valuable insight for future patients. These insights can be harnessed twofold: by means of human analysis and by means of computer analysis. In both cases, data has to be represented unambiguously. Such data facilitates multi-centric comparison, such as e.g. comparing the outcomes of patients who had similar treatment but in different hospitals. Unambiguously defined data facilitates computers to learn from multi-centric, resulting in relevant artificial intelligence.
The ICHOM guide to learn from each other
The ICHOM guide to learn from each other
ICHOM standardizes data definitions and dashboard layout. Hospitals that translate their own data to the ICHOM definitions are ready for benchmarking.
FHIN has the ambition to align with the ICHOM guidelines. The data from the FHIN partners is ready to support physicians to learn from each other.
VOP: the first large scale benchmarking initiative
VOP: the first large scale benchmarking initiative
VOP stands for "Vlaams Oncologie Platform", which is Dutch for "Flemish Oncology Platform". Data definitions and dashboards are developed to compare lung cancer data. It helps pulmonologists to learn from each other and support each other on difficult cases. FHIN will facilitate the benchmarking platforms for pulmonoligists to work together.
Lung cancer treatment effect prediction
Lung cancer treatment effect prediction
Lung cancer in stadium 3b and 4 can be difficult to treat. It is not always clear upfront what the best treatment path is. Therefore, is it possible to train an artificial intelligence model that learns the subtle information from a large number of patients from the past and apply this new knowledge for future patients? The answer is yes. A first model was presented at ELCC 2023. This will be further developed within the FHIN consortium.
Breast cancer treatment support
Breast cancer treatment support
Breast cancer
Presentation in Milan
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