Artificial neural networks condensation: A strategy to facilitate adaption of machine learning in medical settings by reducing computational burden

By Dianbo Liu, Néstor Sepúlveda, Ming Zheng. 2018

7 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Health Information Management, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Dmitriy Dligach
2. Dianbo Liu
3. Timothy Miller
4. Li Huang
5. Yifeng Yin
6. Zeng Fu
7. Hao Deng
8. Shifa Zhang

In Health Information Management

1. Dmitriy Dligach
2. Timothy Miller
3. Dianbo Liu
4. Li Huang
5. Yifeng Yin
6. Zeng Fu
7. Hao Deng
8. Shifa Zhang

In Radiology, Nuclear Medicine and Imaging

1. Timothy Miller
2. Dmitriy Dligach
3. Dianbo Liu

Citations in the corpus

• LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data (2018, methodology): “used a federated transfer learning strategy to balance global and local learning [13–16,20].”
• Two-stage Federated Phenotyping and Patient Representation Learning (2019, methodology): “The strategy of federated learning has been recently adopted in the medical field in structured databased machine learning tasks (Liu et al., 2018a; Huang et al., 2018; Liu et al., 2018b).”