Mixed-Effect-Composite-RNN-Gaussian-Process

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Mixed-Effect-Composite-RNN-Gaussian-Process

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Personalized and Reliable Predictive Models for Healthcare (의료 데이터 기반 신뢰 가능한 개인화된 예측(진단) 모델)

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Mixed Effect Composite RNN-Gaussian Process
==

Mixed Effect Composite RNN-Gaussian Process: Personalized and Reliable Predictive Models for Healthcare

## Reference Code 
The code depends on the Python package named [GPflow](https://github.com/GPflow/GPflow) which implements Gaussian Process models based on tensorflow.

## Reference Paper 
**"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)

## Result  
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.
<p align="center"> 
<img src="https://github.com/OpenXAIProject/Mixed-Effect-Composite-RNN-Gaussian-Process/blob/master/result.png"  width="600">
</p>

## Dataset 
Use your own medical data.

## Installation

**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.

## Requirements 
+ tensorflow
+ scikit-learn
+ GPflow

## License
[Apache License 2.0](https://github.com/OpenXAIProject/Mixed-Effect-Composite-RNN-Gaussian-Process/blob/master/LICENSE "Apache")

## Contacts
If you have any question, please contact Ingyo Chung(jik0730@gmail.com).

# XAI Project

**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)**

+ Project Name : A machine learning and statistical inference framework for explainable artificial intelligence(의사결정 이유를 설명할 수 있는 인간 수준의 학습·추론 프레임워크 개발)

+ Managed by Ministry of Science and ICT/XAIC <img align="right" src="http://xai.unist.ac.kr/static/img/logos/XAIC_logo.png" width=300px>

+ Participated Affiliation : UNIST, Korea Univ., Yonsei Univ., KAIST, AItrics

+ Web Site : <http://openXai.org>

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