mosaicQA - A General Approach to Facilitate Basic Data Quality Assurance for Epidemiological Research

Strategies and techniques for quality control and semantic enrichment with multimodal data: a case study in colorectal cancer with eHDPrep

BACKGROUND

Integration of data from multiple domains can greatly enhance the quality and applicability of knowledge generated in analysis workflows. However, working with health data is challenging, requiring careful preparation in order to support meaningful interpretation and robust results. Ontologies encapsulate relationships between variables that can enrich the semantic content of health datasets to enhance interpretability and inform downstream analyses.

FINDINGS

We developed an R package for electronic health data preparation, "eHDPrep," demonstrated upon a multimodal colorectal cancer dataset (661 patients, 155 variables; Colo-661); a further demonstrator is taken from The Cancer Genome Atlas (459 patients, 94 variables; TCGA-COAD). eHDPrep offers user-friendly methods for quality control, including internal consistency checking and redundancy removal with information-theoretic variable merging. Semantic enrichment functionality is provided, enabling generation of new informative "meta-variables" according to ontological common ancestry between variables, demonstrated with SNOMED CT and the Gene Ontology in the current study. eHDPrep also facilitates numerical encoding, variable extraction from free text, completeness analysis, and user review of modifications to the dataset.

CONCLUSIONS

eHDPrep provides effective tools to assess and enhance data quality, laying the foundation for robust performance and interpretability in downstream analyses. Application to multimodal colorectal cancer datasets resulted in improved data quality, structuring, and robust encoding, as well as enhanced semantic information. We make eHDPrep available as an R package from CRAN (https://cran.r-project.org/package = eHDPrep) and GitHub (https://github.com/overton-group/eHDPrep).

R Packages for Data Quality Assessments and Data Monitoring: A Software Scoping Review with Recommendations for Future Developments

Data quality assessments (DQA) are necessary to ensure valid research results. Despite the growing availability of tools of relevance for DQA in the R language, a systematic comparison of their functionalities is missing. Therefore, we review R packages related to data quality (DQ) and assess their scope against a DQ framework for observational health studies. Based on a systematic search, we screened more than 140 R packages related to DQA in the Comprehensive R Archive Network. From these, we selected packages which target at least three of the four DQ dimensions (integrity, completeness, consistency, accuracy) in a reference framework. We evaluated the resulting 27 packages for general features (e.g., usability, metadata handling, output types, descriptive statistics) and the possible assessment’s breadth. To facilitate comparisons, we applied all packages to a publicly available dataset from a cohort study. We found that the packages’ scope varies considerably regarding functionalities and usability. Only three packages follow a DQ concept, and some offer an extensive rule-based issue analysis. However, the reference framework does not include a few implemented functionalities, and it should be broadened accordingly. Improved use of metadata to empower DQA and user-friendliness enhancement, such as GUIs and reports that grade the severity of DQ issues, stand out as the main directions for future developments.

A method for interoperable knowledge-based data quality assessment

BACKGROUND

Assessing the quality of healthcare data is a complex task including the selection of suitable measurement methods (MM) and adequately assessing their results.

OBJECTIVES

To present an interoperable data quality (DQ) assessment method that formalizes MMs based on standardized data definitions and intends to support collaborative governance of DQ-assessment knowledge, e.g. which MMs to apply and how to assess their results in different situations.

METHODS

We describe and explain central concepts of our method using the example of its first real world application in a study on predictive biomarkers for rejection and other injuries of kidney transplants. We applied our open source tool-openCQA-that implements our method utilizing the openEHR specifications. Means to support collaborative governance of DQ-assessment knowledge are the version-control system git and openEHR clinical information models.

RESULTS

Applying the method on the study's dataset showed satisfactory practicability of the described concepts and produced useful results for DQ-assessment.

CONCLUSIONS

The main contribution of our work is to provide applicable concepts and a tested exemplary open source implementation for interoperable and knowledge-based DQ-assessment in healthcare that considers the need for flexible task and domain specific requirements.