Characterizing and Managing Missing Structured Data in Electronic Health Records

Bk, Beaulieu-Jones; Dr, Lavage; Jw, Snyder; Jh, Moore; Pendergrass, Sarah A.; Cr, Bauer

doi:10.1101/167858

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…We provide the source code to reproduce this work in our repository on GitHub (GitHub, Inc) [ 10 ] under a permissive open source license. In addition, we used continuous analysis [ 11 ] to generate Docker Hub (Docker Inc) images matching the environment of the original analysis and to create intermediate results and logs.…”

Section: Methodsmentioning

confidence: 99%

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Characterizing and Managing Missing Structured Data in Electronic Health Records: Data Analysis

et al. 2018

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BackgroundMissing data is a challenge for all studies; however, this is especially true for electronic health record (EHR)-based analyses. Failure to appropriately consider missing data can lead to biased results. While there has been extensive theoretical work on imputation, and many sophisticated methods are now available, it remains quite challenging for researchers to implement these methods appropriately. Here, we provide detailed procedures for when and how to conduct imputation of EHR laboratory results.ObjectiveThe objective of this study was to demonstrate how the mechanism of missingness can be assessed, evaluate the performance of a variety of imputation methods, and describe some of the most frequent problems that can be encountered.MethodsWe analyzed clinical laboratory measures from 602,366 patients in the EHR of Geisinger Health System in Pennsylvania, USA. Using these data, we constructed a representative set of complete cases and assessed the performance of 12 different imputation methods for missing data that was simulated based on 4 mechanisms of missingness (missing completely at random, missing not at random, missing at random, and real data modelling).ResultsOur results showed that several methods, including variations of Multivariate Imputation by Chained Equations (MICE) and softImpute, consistently imputed missing values with low error; however, only a subset of the MICE methods was suitable for multiple imputation.ConclusionsThe analyses we describe provide an outline of considerations for dealing with missing EHR data, steps that researchers can perform to characterize missingness within their own data, and an evaluation of methods that can be applied to impute clinical data. While the performance of methods may vary between datasets, the process we describe can be generalized to the majority of structured data types that exist in EHRs, and all of our methods and code are publicly available.

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Section: Methodsmentioning

confidence: 99%

“…In addition, we used continuous analysis [ 11 ] to generate Docker Hub (Docker Inc) images matching the environment of the original analysis and to create intermediate results and logs. These artifacts are freely available [ 12 ].…”

Section: Methodsmentioning

confidence: 99%

Characterizing and Managing Missing Structured Data in Electronic Health Records: Data Analysis

et al. 2018

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“…In a recent study, 12 different imputation techniques that were applied to laboratory measures from EHR were compared. In general, authors found that Multivariate Imputation by Chained Equations (MICE) and softImpute consistently imputed missing values with low error [6]; however, in that study, analysis was restricted to 28 most commonly available variables. In another study, authors assessed the different causes of missing data in the EHR data and identified these causes to be the source of unintentional bias [7].…”

Section: Introductionmentioning

confidence: 89%

Latent-Based Imputation of Laboratory Measures from Electronic Health Records: Case for Complex Diseases

Abedi

Shivakumar

et al. 2018

Preprint

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Abstract:Imputation is a key step in Electronic Health Records-mining as it can significantly affect the conclusions derived from the downstream analysis. There are three main categories that explain the missingness in clinical settings ̶ incompleteness, inconsistency, and inaccuracy ̶ and these can capture a variety of situations: the patient did not seek treatment, the health care provider did not enter the information, etc. We used EHR data from patients diagnosed with Inflammatory Bowel Disease from Geisinger Health System to design a novel imputation that focuses on a complex phenotype. Our approach is based on latent-based analysis integrated with clustering to group patients based on their comorbidities before imputation. IBD is a chronic illness of unclear etiology and without a complete cure. We have taken advantage of the complexity of IBD to preprocess the EHR data of 10,498 IBD patients and show that imputation can be improved using shared latent comorbidities. The R code and sample simulated input data will be available at a future time.

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“…Second, considerations for the choice of imputation strategy need to be made. While there is no such thing as best imputation method, comparisons of error, bias, and implementation difficulty can be leveraged in making a knowledgeable choice[60]. This, however, comes with the caveat that such conclusions may not be generalizable between different datasets.…”

Section: Challengesmentioning

confidence: 99%

Informatics and machine learning to define the phenotype

Basile

Ritchie

2018

Expert Review of Molecular Diagnostics

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Introduction For the past decade, the focus of complex disease research has been the genotype. From technological advancements to the development of analysis methods, great progress has been made. However, advances in our definition of the phenotype have remained stagnant. Phenotype characterization has recently emerged as an exciting area of informatics and machine learning. The copious amounts of diverse biomedical data that have been collected may be leveraged with data-driven approaches to elucidate trait-related features and patterns. Areas covered In this review, the authors discuss the phenotype in traditional genetic associations and the challenges this has imposed. The authors address approaches for phenotype refinement that can aid in the more accurate characterization of traits. Further, the authors highlight promising machine learning approaches for establishing a phenotype and the challenges of electronic health record (EHR) derived data. Expert Commentary The authors hypothesize that through unsupervised machine learning, data-driven approaches can be used to define phenotypes rather than relying on expert clinician knowledge, which may be inaccurate. Through the use of machine learning and an unbiased set of features extracted from clinical repositories, researchers will have the potential to further understand complex traits and identify patient subgroups. This knowledge may lead to more preventative and precise clinical care.

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Characterizing and Managing Missing Structured Data in Electronic Health Records

Cited by 5 publications

References 20 publications

Characterizing and Managing Missing Structured Data in Electronic Health Records: Data Analysis

Characterizing and Managing Missing Structured Data in Electronic Health Records: Data Analysis

Latent-Based Imputation of Laboratory Measures from Electronic Health Records: Case for Complex Diseases

Informatics and machine learning to define the phenotype

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