Estimation of conditional mixture Weibull distribution with right-censored data using neural network for time-to-event analysis
Paper link This paper extends classical parametric time-to-event models (a part of Survival Analysis (SA)) using a neural network (NN) architecture. While providing the implementation, we will be reviewing the following concepts: Theoretical concepts: Survival analysis Parametric models Maximum Likelihood estimate Drawing random numbers from a given distribution Implementation details ( colab, p=1 and colab, any p ): Custom loss and architecture with Tensorflow How to use Tensorflow probability as an alternative to build the model architecture and its loss Time-to-event prediction with simulated data (the ones discussed in the paper cf. Figure 4) The key take away of this article is how to leverage Tensorflow Probability for survival analysis. Using the framework removes a lot of pain, typically yielding a code base that is easy to modify and that can accomodate every setting described in this article (i.e using a Mixture or not, using the Weibull or some other distribution). ...