DL-based survival analysis for high dimensional data about pycox HOT 4 CLOSED

-- Yu-Dong Zhang, M.D. Department of Radiology, the First Affiliated Hospital with Nanjing Medical University Nanjing, China, 210009 E-mail: [email protected] At 2019-11-28 01:47:26, "Haavard Kvamme" <[email protected]> wrote: As this was not an issue with the code, but instead a specific question of modeling, it was answered through email. We had a nice conversation. A summary of our conversation: One cannot really expect neural networks to perform well with that many input variables and that few samples. So some reduction of the feature space is necessary and an AE might be a reasonable approach. For the record, the input is not an image. One can create an architecture that combines the AE with the N-MTLR by letting the network have two outputs and use a loss function that is a weighted sum of the AE loss and the survival loss. I'm planning to give an example of this in the future, as it nicely illustrates how one can extend the implemented models. This is a reference to the interpretation of the estimated parameters of a Cox proportional hazards regression with a linear risk function. There is not an equivalent interpretation of the parameters of a neural network. The survival estimates are typically a pandas DataFrame, at least if they are obtained with surv = model.predict_surv_df(x), meaning they can be stored to a csv file with for example surv.to_csv('myfile.csv'). — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub, or unsubscribe.