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

Tom M Toner, Rashi Pancholi, Paul Miller, Thorsten Forster, Helen G Coleman, Ian M Overton*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

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).


Original languageEnglish
Article numbergiad030
Number of pages14
JournalGigascience
Volume12
Early online date12 May 2023
DOIs
Publication statusEarly online date - 12 May 2023

Keywords

  • Digital Health
  • Bioinformatics
  • Ontologies
  • health data
  • Quality Control
  • Colorectal Neoplasms
  • Medical Informatics
  • Semantic integration
  • colorectal cancer
  • quality control
  • Data Accuracy
  • Humans
  • Colorectal Neoplasms - genetics
  • semantic enrichment
  • medical informatics
  • Semantics
  • quality assessment
  • bioinformatics
  • data integration
  • ontology
  • Gene Ontology

ASJC Scopus subject areas

  • Software
  • Health Information Management
  • Health Informatics
  • Oncology
  • Epidemiology

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