torchsurv and pysurvival
These are **competitors**: both provide Python frameworks for training machine learning models on censored survival data, with torchsurv emphasizing deep learning via PyTorch while pysurvival offers a broader range of classical and modern algorithms, requiring practitioners to choose one as their primary survival analysis toolkit.
About torchsurv
Novartis/torchsurv
Deep survival analysis made easy
This tool helps researchers and clinicians analyze time-to-event data, common in medical studies or reliability engineering. You provide patient data, including whether an event occurred and the time until it happened (or censoring time), along with relevant covariates. The tool outputs predictions about survival likelihoods or event risks over time. It's designed for quantitative researchers, biostatisticians, and data scientists working on deep learning models.
About pysurvival
square/pysurvival
Open source package for Survival Analysis modeling
This tool helps you predict when a specific event is likely to occur, such as customer churn or loan default. You provide historical data on events and their timings, and it outputs models that estimate event probabilities over time. Data scientists, risk analysts, and marketing strategists can use this to understand and forecast 'time-to-event' scenarios.
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