Using classification models for the generation of disease-specific medications from biomedical literature and clinical data repository.

ABSTRACT:

Objective

Mining disease-specific associations from existing knowledge resources can be useful for building disease-specific ontologies and supporting knowledge-based applications. Many association mining techniques have been exploited. However, the challenge remains when those extracted associations contained much noise. It is unreliable to determine the relevance of the association by simply setting up arbitrary cut-off points on multiple scores of relevance; and it would be expensive to ask human experts to manually review a large number of associations. We propose that machine-learning-based classification can be used to separate the signal from the noise, and to provide a feasible approach to create and maintain disease-specific vocabularies.

Method

We initially focused

SUBMITTER: Wang L 

PROVIDER: S-EPMC5509335 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature