I‘ve used it in production as well, but thought that there is quite few research published about it lately. So I kind of assumed it’s similar in practice.

I’ll look into it, thanks a lot! The main topic/question is still open for me tho.

For me this doesn’t really make all that much sense. Stacking is not a tool in itself, it’s rather a modelling approach. Also from what I can tell in the h2o docs it seems that their Stacked Ensemble has only one level to it and no meta-model. Thus rather than using e. g. the outputs (or probability distribution) of each model to train a new model, it will only use the output for any given input directly to return its final prediction, doesn’t it?

If I understood correctly, rather than actually predicting survival time your goal is to see whether certain parameters have influence on the survival time. Besides the other things already mentioned, one approach might be trying to predict survival time via a black box algorithm and trying to explain the decision of the algorithm. One paper that covers this topic would be this one.

You would need to preprocess the pictures anyway, so it should be quite easy to only get text back, especially if your labels are already formatted the way you want your output to be. This would probably mean you only need to decode your output.

Keep in mind that manually labelling a large set of data is very tedious, but your approach of building an app sounds like a good solution.

Usually it is indeed the case that especially deep learning requires a lot of data. However also with little data neural networks might generate good results using the implementations provided by e. g. scikit learn. Also you might want to look into Boosting Algorithms (specifically XGBoost), as they are usually powerful on tabular data which is a common type of data to have in businesses.