Mixed Effect Composite RNN-Gaussian Process: Personalized and Reliable Predictive Models for Healthcare
The code depends on the Python package named GPflow which implements Gaussian Process models based on tensorflow.
"Mixed Effect Composite RNN-GP: A Personalized and Reliable Prediction Model for Healthcare". Ingyo Chung, Saehoon Kim, Juho Lee, Sung Ju Hwang, and Eunho Yang (https://arxiv.org/abs/1806.01551)
We conducted experiments on diverse set of disease risk prediction tasks based on medical check-up features. Results shown below show that our model is superior to other baseline models.
Use your own medical data.
1. Fork & Clone : Fork this project to your repository and clone to your work directory.
$ https://github.com/OpenXAIProject/Mixed-Effect-Composite-RNN-Gaussian-Process.git
2. Run : Run "run_mecgp.py" with appropriate arguments and well-formatted dataset.
- tensorflow
- scikit-learn
- GPflow
If you have any question, please contact Ingyo Chung(jik0730@gmail.com).
This work was supported by Institute for Information & Communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No.2017-0-01779, A machine learning and statistical inference framework for explainable artificial intelligence)
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Project Name : A machine learning and statistical inference framework for explainable artificial intelligence(의사결정 이유를 설명할 수 있는 인간 수준의 학습·추론 프레임워크 개발)
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Participated Affiliation : UNIST, Korea Univ., Yonsei Univ., KAIST, AItrics
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Web Site : http://openXai.org