Extracting medication information from unstructured public health data: a demonstration on data from population-based and tertiary-based samples.

ABSTRACT:

Background

Unstructured data from clinical epidemiological studies can be valuable and easy to obtain. However, it requires further extraction and processing for data analysis. Doing this manually is labor-intensive, slow and subject to error. In this study, we propose an automation framework for extracting and processing unstructured data.

Methods

The proposed automation framework consisted of two natural language processing (NLP) based tools for unstructured text data for medications and reasons for medication use. We first checked spelling using a spell-check program trained on publicly available knowledge sources and then applied NLP techniques. We mapped medication names into generic names using vocabulary from publicly available knowledge sources. We used WHO's Anatomi

SUBMITTER: Chen R 

PROVIDER: S-EPMC7559204 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature