pycox and torchsurv
These are competitors: both provide PyTorch-based frameworks for deep survival analysis with overlapping functionality (neural network architectures, loss functions, evaluation metrics), so practitioners typically choose one or the other based on API design and community maturity rather than using them together.
About pycox
havakv/pycox
Survival analysis with PyTorch
This tool helps researchers, clinicians, and data scientists predict the time until an event occurs, such as disease recurrence, customer churn, or machine failure. You provide data including individual characteristics and observed event times, and it outputs models that estimate the probability of the event happening over time. It's designed for anyone working with 'time-to-event' data who needs to forecast when things will happen.
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.
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