A Data Element-Function Conceptual Model for Data Quality Checks.

ABSTRACT:

Introduction

In aggregate, existing data quality (DQ) checks are currently represented in heterogeneous formats, making it difficult to compare, categorize, and index checks. This study contributes a data element-function conceptual model to facilitate the categorization and indexing of DQ checks and explores the feasibility of leveraging natural language processing (NLP) for scalable acquisition of knowledge of common data elements and functions from DQ checks narratives.

Methods

The model defines a "data element", the primary focus of the check, and a "function", the qualitative or quantitative measure over a data element. We applied NLP techniques to extract both from 172 checks for Observational Health Data Sciences and Informatics (OHDSI) and 3,434 checks for Kaiser Per

SUBMITTER: Rogers JR 

PROVIDER: S-EPMC6484368 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature