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Currently submitted to: JMIR Formative Research

Date Submitted: May 28, 2020
Open Peer Review Period: May 28, 2020 - Jul 14, 2020
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Using artificial neural network condensation to facilitate adaption of machine learning in medical settings by reducing computational burden

  • Dianbo Liu; 

ABSTRACT

Background:

Applications of machine learning (ML) on health care can have a great impact on people’s lives. At the same time, medical data is usually big, requiring a significant amount of 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 and on mobile devices. This can prevent many people from benefiting of the advancement in ML applications for healthcare.

Objective:

In this paper we explored three methods to increase computational efficiency of either recurrent neural net-work(RNN) or feedforward (deep) neural network (DNN) while not compromising its accuracy. We used in-patient mortality prediction as our case analysis upon intensive care dataset.

Methods:

We reduced the size of RNN and DNN by applying pruning of “unused” neurons. Additionally, we modified the RNN structure by adding a hidden-layer to the RNN cell but reduce the total number of recurrent layers to accomplish a reduction of total parameters in the network. Finally, we implemented quantization on DNN—forcing the weights to be 8-bits instead of 32-bits.

Results:

We found that all methods increased implementation efficiency–including training speed, memory size and inference speed–without reducing the accuracy of mortality prediction.

Conclusions:

This improvements allow the implementation of sophisticated NN algorithms on devices with lower computational resources.


 Citation

Please cite as:

Liu D

Using artificial neural network condensation to facilitate adaption of machine learning in medical settings by reducing computational burden

JMIR Preprints. 28/05/2020:20767

DOI: 10.2196/preprints.20767

URL: https://preprints.jmir.org/preprint/20767

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