DNorm: disease name normalization with pairwise learning to rank.

ABSTRACT:

Motivation

Despite the central role of diseases in biomedical research, there have been much fewer attempts to automatically determine which diseases are mentioned in a text-the task of disease name normalization (DNorm)-compared with other normalization tasks in biomedical text mining research.

Methods

In this article we introduce the first machine learning approach for DNorm, using the NCBI disease corpus and the MEDIC vocabulary, which combines MeSH® and OMIM. Our method is a high-performing and mathematically principled framework for learning similarities between mentions and concept names directly from training data. The technique is based on pairwise learning to rank, which has not previously been applied to the normalization task but has proven successful in large opt

SUBMITTER: Leaman R 

PROVIDER: S-EPMC3810844 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature