Active learning: a step towards automating medical concept extraction.

ABSTRACT:

Objective

This paper presents an automatic, active learning-based system for the extraction of medical concepts from clinical free-text reports. Specifically, (1) the contribution of active learning in reducing the annotation effort and (2) the robustness of incremental active learning framework across different selection criteria and data sets are determined.

Materials and methods

The comparative performance of an active learning framework and a fully supervised approach were investigated to study how active learning reduces the annotation effort while achieving the same effectiveness as a supervised approach. Conditional random fields as the supervised method, and least confidence and information density as 2 selection criteria for active learning framework were used. The

SUBMITTER: Kholghi M 

PROVIDER: S-EPMC7784313 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature