ArticleJournal of the American Medical Informatics Association : JAMIA2019
Integration of genetic and clinical information to improve imputation of data missing from electronic health records.
Article in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Prediction Pipeline Selection for Incomplete Clinical Data via Missingness Fingerprints and Instance Augmentation.Bioengineering (Basel, Switzerland) · 2026Article
- Reducing Information and Selection Bias in EHR-Linked Biobanks via Genetics-Informed Multiple Imputation and Sample Weighting.medRxiv : the preprint server for health sciences · 2024Article
- A deep learning transformer model predicts high rates of undiagnosed rare disease in large electronic health systems.medRxiv : the preprint server for health sciences · 2023Article
- Creation of a structured molecular genomics report for Germany as a local adaption of HL7's Genomic Reporting Implementation Guide.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Machine learning approaches for electronic health records phenotyping: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Using set visualisation to find and explain patterns of missing values: a case study with NHS hospital episode statistics data.BMJ open · 2022Article
- The relationship of endothelial function and arterial stiffness with subclinical target organ damage in essential hypertension.Journal of clinical hypertension (Greenwich, Conn.) · 2022Article
- A narrative review on the validity of electronic health record-based research in epidemiology.BMC medical research methodology · 2021Review
- Importance-aware personalized learning for early risk prediction using static and dynamic health data.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Increasing the Density of Laboratory Measures for Machine Learning Applications.Journal of clinical medicine · 2020Article
- Electronic health records and polygenic risk scores for predicting disease risk.Nature reviews. Genetics · 2020Review
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Authors and funding
3 authors.
Funding
Abstract
objectiveClinical data of patients' measurements and treatment history stored in electronic health record (EHR) systems are starting to be mined for better treatment options and disease associations. A primary challenge associated with utilizing EHR data is the considerable amount of missing data. Failure to address this issue can introduce significant bias in EHR-based research. Currently, imputation methods rely on correlations among the structured phenotype variables in the EHR. However, genetic studies have shown that many EHR-based phenotypes have a heritable component, suggesting that measured genetic variants might be useful for imputing missing data. In this article, we developed a computational model that incorporates patients' genetic information to perform EHR data imputation. MATERIALS AND
methodsWe used the individual single nucleotide polymorphism's association with phenotype variables in the EHR as input to construct a genetic risk score that quantifies the genetic contribution to the phenotype. Multiple approaches to constructing the genetic risk score were evaluated for optimal performance. The genetic score, along with phenotype correlation, is then used as a predictor to impute the missing values.
resultsTo demonstrate the method performance, we applied our model to impute missing cardiovascular related measurements including low-density lipoprotein, heart failure, and aortic aneurysm disease in the electronic Medical Records and Genomics data. The integration method improved imputation's area-under-the-curve for binary phenotypes and decreased root-mean-square error for continuous phenotypes.
conclusionCompared with standard imputation approaches, incorporating genetic information offers a novel approach that can utilize more of the EHR data for better performance in missing data imputation.
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