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

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

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset