Survival analysis in Python

Survival analysis built on top of scikit-learn

Survival analysis with PyTorch

Reliability engineering toolkit for Python - https://reliability.readthedocs.io/en/latest/

Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events

Improving XGBoost survival analysis with embeddings and debiased estimators

A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python

Deep Recurrent Survival Analysis, an auto-regressive deep model for time-to-event data analysis with censorship handling. An implementation of our AAAI 2019 paper and a benchmark for several (Python) implemented survival analysis methods.

A system for automating the design of predictive modeling pipelines tailored for clinical prognosis.

COX Proportional risk model and survival analysis implemented by tensorflow.

Deep learning for flexible market price modeling (landscape forecasting) in real-time bidding advertising. An implementation of our KDD 2019 paper with some other (Python) implemented prediction models.

SALMON: Survival Analysis Learning with Multi-Omics Neural Networks

Deep survival analysis made easy

A Random Survival Forest implementation for python inspired by Ishwaran et al. - Easily understandable, adaptable and extendable.

Competing Risks and Survival Analysis

Scripts for https://www.nature.com/articles/s41598-018-27707-4, using Convolutional Neural Network to detect lung cancer tumor area

Implementation of DeepSurv using Keras

A Python package for survival analysis. The most flexible survival analysis package available. SurPyval can work with arbitrary combinations of observed, censored, and truncated data. SurPyval can also fit distributions with 'offsets' with ease, for example the three parameter Weibull distribution.

SurvTRACE: Transformers for Survival Analysis with Competing Events

ICML 2018: "Adversarial Time-to-Event Modeling"