  Machine Learning (ML) applications on healthcare can have a great impact on
people's lives helping deliver better and timely treatment to those in need. At
the same time, medical data is usually big and sparse requiring important
computational resources. Although it might not be a problem for wide-adoption
of ML tools in developed nations, availability of computational resource can
very well be limited in third-world nations. This can prevent the less favored
people from benefiting of the advancement in ML applications for healthcare. In
this project we explored methods to increase computational efficiency of ML
algorithms, in particular Artificial Neural Nets (NN), while not compromising
the accuracy of the predicted results. We used in-hospital mortality prediction
as our case analysis based on the MIMIC III publicly available dataset. We
explored three methods on two different NN architectures. We reduced the size
of recurrent neural net (RNN) and dense neural net (DNN) by applying pruning of
"unused" neurons. Additionally, we modified the RNN structure by adding a
hidden-layer to the LSTM cell allowing to use less recurrent layers for the
model. Finally, we implemented quantization on DNN forcing the weights to be
8-bits instead of 32-bits. We found that all our methods increased
computational efficiency without compromising accuracy and some of them even
achieved higher accuracy than the pre-condensed baseline models.
