/Mixed-Effect-Composite-RNN-Gaussian-Process

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

Primary LanguageJupyter NotebookApache License 2.0Apache-2.0

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 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.

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

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

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

  • Web Site : http://openXai.org