Lrebaud/ICARE
Individual Coefficient Approximation for Risk Estimation (ICARE) model
This tool helps medical researchers and clinicians predict patient outcomes or risk levels from medical imaging data. You provide it with a dataset of patient features (like radiomics from PET/CT scans) and it outputs a risk score or classification, indicating a patient's prognosis. It's designed for anyone working with clinical data to make predictive models, especially in oncology.
No commits in the last 6 months. Available on PyPI.
Use this if you need a robust model to predict survival, patient outcomes, or classify risk from high-dimensional medical data, such as radiomics, without extensive feature engineering.
Not ideal if you require interpretable coefficients for each feature or if your primary task is regression with calibrated output, as its ranking models are not calibrated.
Stars
18
Forks
1
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Sep 09, 2023
Commits (30d)
0
Dependencies
5
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