Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning

Hao-Cheng Kao, Kai-Fu Tang, Edward Y. Chang

AAAI 2018 pp. 2305-2313

doi:10.1609/AAAI.V32I1.11902 /aaai/2018/kao2018aaai-context/

Abstract

Online symptom checkers have been deployed by sites such as WebMD and Mayo Clinic to identify possible causes and treatments for diseases based on a patient’s symptoms. Symptom checking first assesses a patient by asking a series of questions about their symptoms, then attempts to predict potential diseases. The two design goals of a symptom checker are to achieve high accuracy and intuitive interactions. In this paper we present our context-aware hierarchical reinforcement learning scheme, which significantly improves accuracy of symptom checking over traditional systems while also making a limited number of inquiries.

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Cite

Text

Kao et al. "Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning." AAAI Conference on Artificial Intelligence, 2018. doi:10.1609/AAAI.V32I1.11902

Markdown

[Kao et al. "Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning." AAAI Conference on Artificial Intelligence, 2018.](https://mlanthology.org/aaai/2018/kao2018aaai-context/) doi:10.1609/AAAI.V32I1.11902

BibTeX

@inproceedings{kao2018aaai-context,
  title     = {{Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning}},
  author    = {Kao, Hao-Cheng and Tang, Kai-Fu and Chang, Edward Y.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2018},
  pages     = {2305-2313},
  doi       = {10.1609/AAAI.V32I1.11902},
  url       = {https://mlanthology.org/aaai/2018/kao2018aaai-context/}
}