i6092467/vadesc
A probabilistic model to cluster survival data in a variational deep clustering setting
This project helps medical researchers and clinicians uncover hidden patient subpopulations from their medical records. By analyzing patient characteristics and survival times, it identifies groups that might respond differently to treatments or have varying disease progressions. You input patient data including demographics, clinical factors, and observed survival times (which can be incomplete), and it outputs distinct patient clusters with their unique survival patterns.
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Use this if you need to identify hidden patient groups with different disease characteristics and survival outcomes from clinical data to improve personalized treatment or disease understanding.
Not ideal if you are looking for a straightforward predictive model without needing to discover underlying patient segments or if your data does not include survival time information.
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Last pushed
Aug 03, 2022
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