Adaptive semantic tag mining from heterogeneous clinical research texts.

ABSTRACT:

Objectives

To develop an adaptive approach to mine frequent semantic tags (FSTs) from heterogeneous clinical research texts.

Methods

We develop a "plug-n-play" framework that integrates replaceable unsupervised kernel algorithms with formatting, functional, and utility wrappers for FST mining. Temporal information identification and semantic equivalence detection were two example functional wrappers. We first compared this approach's recall and efficiency for mining FSTs from ClinicalTrials.gov to that of a recently published tag-mining algorithm. Then we assessed this approach's adaptability to two other types of clinical research texts: clinical data requests and clinical trial protocols, by comparing the prevalence trends of FSTs across three texts.

Results

Our app

SUBMITTER: Hao T 

PROVIDER: S-EPMC4441820 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature