Integration of genetic and clinical information to improve imputation of data missing from electronic health records

Abstract

Objective Clinical 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 Methods We 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. Results To 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. Conclusion Compared 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.

A narrative review on the validity of electronic health record-based research in epidemiology

“…≥n visits required to be eligible into cohort); however, this may exclude a substantial amount of patients with certain characteristics, incurring a selection bias or limiting the generalizability of study findings (see Challenge #1 ). Other strategies attempt to account for missing visit biases through longitudinal imputation approaches; for example, if a patient missed a visit, a disease activity score can be imputed for that point in time, given other data points [ 73 , 74 ]. Surrogate measures may also be used to infer patient outcomes, such as controlling for “informative” missingness as an indicator variable or using actual number of missed visits that were scheduled as a proxy for external circumstances influencing care [ 20 ].…”

Detecting SNP–SNP Interactions in Imbalanced Case-Control Study

“…In MOMDR, the Pareto cross-validation consistency (CVC) operation is used to determine the multiple solutions depending on the most instances of solutions in K Pareto sets. MOMDR process comprises these stages: (1) The dataset is divided into K subsets for the CV calculation and generate K Pareto sets. (2) All feasible m-locus combinations are generated, and data reduction technique is used to divide multilocus genotypes as high-and low-risk groups.…”

“…Genome-wide association studies (GWASs) have demonstrated that multilocus single-nucleotide polymorphisms (SNPs) influence some diseases [1]- [5]. SNP-SNP interactions might be involved in some complex traits of these diseases [6]- [8], and the determination of SNP-SNP interactions could resolve missing heritability concerns [9].…”

Increasing the Density of Laboratory Measures for Machine Learning Applications

“…For example, the missingness level for very important variables, such as hemoglobin A1C or HbA1c (LOINC ID: 17856-6) levels, a common biomarker for diabetes can easily reach 50% or more in many realistic large datasets. At last, in a more recent study, the integration of genetic and clinical information was shown to improve the imputation of data missing from the Electronic Health Records [ 13 ]; however, genetic data integrated with the EHR is still scarce.…”