Corpus ID: 35812678

Inquire and Diagnose : Neural Symptom Checking Ensemble using Deep Reinforcement Learning

@inproceedings{Tang2016InquireAD,
  title={Inquire and Diagnose : Neural Symptom Checking Ensemble using Deep Reinforcement Learning},
  author={Kai-Fu Tang},
  year={2016}
}
This work proposes a novel symptom checker: an ensemble neural network model that learns to inquire symptoms and diagnose diseases. The ensemble model consists of several small anatomical models that are responsible for different anatomical parts. Compared to the traditional single monolithic model approach, our ensemble approach obtains markedly higher disease-prediction accuracy. 
23 Citations
Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning
  • 28
  • PDF
A Model-Based Reinforcement Learning Approach for a Rare Disease Diagnostic Task
  • 5
  • Highly Influenced
  • PDF
SeqMed: Recommending Medication Combination with Sequence Generative Adversarial Nets
  • Shuai Wang
  • Computer Science
  • 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
  • 2020
Deep Reinforcement Learning for Clinical Decision Support: A Brief Survey
  • 8
  • Highly Influenced
  • PDF
Task-oriented Dialogue System for Automatic Disease Diagnosis via Hierarchical Reinforcement Learning
  • 2
  • PDF
Reinforcement Learning in Healthcare: A Survey
  • 46
  • PDF
...
1
2
3
...

References

SHOWING 1-8 OF 8 REFERENCES
Playing Atari with Deep Reinforcement Learning
  • 5,013
  • Highly Influential
  • PDF
Inductive and Bayesian learning in medical diagnosis
  • 316
Machine learning for medical diagnosis: history, state of the art and perspective
  • I. Kononenko
  • Computer Science, Medicine
  • Artif. Intell. Medicine
  • 2001
  • 996
  • PDF
Evaluation of symptom checkers for self diagnosis and triage: audit study
  • 207
  • PDF
Reinforcement Learning: An Introduction
  • 28,251
  • PDF
Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid
  • 1,320
  • PDF
Machine learning for medical diagnosis : history , state of the art and perspective
  • Artificial Intelligence in Medicine
  • 2001