Corpus ID: 227247972

FIT: a Fast and Accurate Framework for Solving Medical Inquiring and Diagnosing Tasks

@article{He2020FITAF,
  title={FIT: a Fast and Accurate Framework for Solving Medical Inquiring and Diagnosing Tasks},
  author={Weijie He and Xiaohao Mao and Chao Ma and Jos{\'e} Miguel Hern{\'a}ndez-Lobato and Ting Chen},
  journal={ArXiv},
  year={2020},
  volume={abs/2012.01065}
}
Automatic self-diagnosis provides low-cost and accessible healthcare via an agent that queries the patient and makes predictions about possible diseases. From a machine learning perspective, symptom-based self-diagnosis can be viewed as a sequential feature selection and classification problem. Reinforcement learning methods have shown good performance in this task but often suffer from large search spaces and costly training. To address these problems, we propose a competitive framework… Expand
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SHOWING 1-10 OF 28 REFERENCES
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
TLDR
In EDDI, a novel partial variational autoencoder to predict missing data entries problematically given any subset of the observed ones, and combine it with an acquisition function that maximizes expected information gain on a set of target variables is proposed. Expand
Joint Active Feature Acquisition and Classification with Variable-Size Set Encoding
TLDR
This work forms this active feature acquisition as a jointly learning problem of training both the classifier (environment) and the RL agent that decides either to `stop and predict' or `collect a new feature' at test time, in a cost-sensitive manner. Expand
REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis
TLDR
REFUEL, a reinforcement learning method with two techniques: reward shaping and feature rebuilding, is proposed to improve the performance of online symptom checking for disease diagnosis. Expand
Generative Adversarial Regularized Mutual Information Policy Gradient Framework for Automatic Diagnosis
TLDR
A new policy gradient framework based on the Generative Adversarial Network (GAN) to optimize the RL model for automatic diagnosis and beats the state-of-art methods, not only can achieve higher diagnosis accuracy, but also can use a smaller number of inquires to make diagnosis decision. Expand
Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning
TLDR
This paper presents a context-aware hierarchical reinforcement learning scheme, which significantly improves accuracy of symptom checking over traditional systems while also making a limited number of inquiries. Expand
Machine learning for medical diagnosis: history, state of the art and perspective
  • I. Kononenko
  • Computer Science, Medicine
  • Artif. Intell. Medicine
  • 2001
The paper provides an overview of the development of intelligent data analysis in medicine from a machine learning perspective: a historical view, a state-of-the-art view, and a view on some future… Expand
Inquire and Diagnose : Neural Symptom Checking Ensemble using Deep Reinforcement Learning
TLDR
This work proposes a novel symptom checker: an ensemble neural network model that learns to inquire symptoms and diagnose diseases that obtains markedly higher disease-prediction accuracy. Expand
Adam: A Method for Stochastic Optimization
TLDR
This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Expand
Feature-Budgeted Random Forest
TLDR
A novel random forest algorithm to minimize prediction error for a user-specified feature acquisition budget and demonstrate superior accuracy-cost curves against state-of-the-art prediction-time algorithms. Expand
Inductive and Bayesian learning in medical diagnosis
TLDR
Surprisingly, the naive Bayesian classifier is superior to Assistant in classification accuracy and explanation ability, while the interpretation of the acquired knowledge seems to be equally valuable. Expand
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