Medical symptom recognition from patient text: An active learning approach for long-tailed multilabel distributions

11/12/2020 ∙ by Ali Mottaghi, et al. ∙ 29 ∙ share

We study the problem of medical symptoms recognition from patient text, for the purposes of gathering pertinent information from the patient (known as history-taking). We introduce an active learning method that leverages underlying structure of a continually refined, learned latent space to select the most informative examples to label. This enables the selection of the most informative examples that progressively increases the coverage on the universe of symptoms via the learned model, despite the long tail in data distribution.

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Medical symptom recognition from patient text: An active learning approach for long-tailed multilabel distributions

Authors

Learning how to Active Learn: A Deep Reinforcement Learning Approach

Deep Active Learning via Open Set Recognition

Task-Aware Variational Adversarial Active Learning

Active Learning for Speech Recognition: the Power of Gradients

Active Learning in the Overparameterized and Interpolating Regime

The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning

Probabilistic Active Learning of Functions in Structural Causal Models

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