Corpus ID: 2159236

An Application of Inverse Reinforcement Learning to Medical Records of Diabetes Treatment

@inproceedings{Asoh2013AnAO,
  title={An Application of Inverse Reinforcement Learning to Medical Records of Diabetes Treatment},
  author={Hideki Asoh and Masanori Shiro and Shotaro Akaho and Toshihiro Kamishima},
  year={2013}
}
  • Hideki Asoh, Masanori Shiro, +1 author Toshihiro Kamishima
  • Published 2013
  • Computer Science
  • It is an important issue to utilize large amount of medical records which are being accumulated on medical information systems to improve the quality of medical treatment. The process of medical treatment can be considered as a sequential interaction process between doctors and patients. From this viewpoint, we have been modeling medical records using Markov decision processes (MDPs). Using our model, we can simulate the future of each patient and evaluate each treatment. In order to do so, the… CONTINUE READING

    Figures, Tables, and Topics from this paper.

    Explore key concepts

    Links to highly relevant papers for key concepts in this paper:

    Citations

    Publications citing this paper.
    SHOWING 1-10 OF 14 CITATIONS

    Optimal medication dosing from suboptimal clinical examples: A deep reinforcement learning approach

    VIEW 1 EXCERPT
    CITES METHODS

    Deep Reinforcement Learning for Clinical Decision Support: A Brief Survey

    VIEW 5 EXCERPTS
    CITES METHODS & BACKGROUND
    HIGHLY INFLUENCED

    Learning models for writing better doctor prescriptions

    VIEW 1 EXCERPT
    CITES METHODS

    Reinforcement Learning in Healthcare: A Survey

    VIEW 2 EXCERPTS
    CITES BACKGROUND

    State Distribution-Aware Sampling for Deep Q-Learning

    VIEW 1 EXCERPT
    CITES METHODS

    CDSS-RM: a clinical decision support system reference model

    VIEW 1 EXCERPT
    CITES METHODS