Active learning reduces annotation time for clinical concept extraction.

Objective: To investigate: (1) the annotation time savings by various active learning query strategies compared to supervised learning and a random sampling baseline, and (2) the benefits of active learning-assisted pre-annotations in accelerating the manual annotation process compared to de novo annotation.

Materials And Methods: There are 73 and 120 discharge summary reports provided by Beth Israel institute in the train and test sets of the concept extraction task in the i2b2/VA 2010 challenge, respectively. The 73 reports were used in user study experiments for manual annotation.

Active learning: a step towards automating medical concept extraction.

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.

Towards semantic search and inference in electronic medical records: An approach using concept--based information retrieval.

Background: This paper presents a novel approach to searching electronic medical records that is based on concept matching rather than keyword matching.

Aim: The concept-based approach is intended to overcome specific challenges we identified in searching medical records.

Method: Queries and documents were transformed from their term-based originals into medical concepts as defined by the SNOMED-CT ontology.