Strategies for Handling Missing Data in Electronic Health Record Derived Data
By B. Wells, Kevin Chagin, A. Nowacki, M. Kattan. eGEMs, 2013
428 citations recorded by the source corpus.
Electronic health records (EHRs) present a wealth of data that are vital for improving patient-centered outcomes, although the data can present significant statistical challenges. In particular, EHR data contains substantial missing information that if left unaddressed could reduce the validity of conclusions drawn. Properly addressing the missing data issue in EHR data is complicated by the fact that it is sometimes difficult to differentiate between missing data and a negative value. For example, a patient without a documented history of heart failure may truly not have disease or the clinician may have simply not documented the condition. Approaches for reducing missing data in EHR systems come from multiple angles, including: increasing structured data documentation, reducing data input errors, and utilization of text parsing / natural language processing. This paper focuses on the analytical approaches for handling missing data, primarily multiple imputation. The broad range of variables available in typical EHR systems provide a wealth of information for mitigating potential biases caused by missing data. The probability of missing data may be linked to disease severity and healthcare utilization since unhealthier patients are more likely to have comorbidities and each interaction with the health care system provides an opportunity for documentation. Therefore, any imputation routine should include predictor variables that assess overall health status (e.g. Charlson Comorbidity Index) and healthcare utilization (e.g. number of encounters) even when these comorbidities and patient encounters are unrelated to the disease of interest. Linking the EHR data with other sources of information (e.g. National Death Index and census data) can also provide less biased variables for imputation. Additional methodological research with EHR data and improved epidemiological training of clinical investigators is warranted.
Subfields: Artificial Intelligence, Epidemiology, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/b021b9fc3bef24a3769098f9b7e3b2fa25ae7f63
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In Artificial Intelligence
- David A. Sontag
- Yan Liu
- Marzyeh Ghassemi
- Sanjay Purushotham
- Zhengping Che
- Kyunghyun Cho
- Yinhai Wang
- Ruimin Ke
In Epidemiology
In Management Science and Operations Research
- Sanjay Purushotham
- Zhiyong Cui
- David A. Sontag
- Zhengping Che
- Kyunghyun Cho
- Marzyeh Ghassemi
- Ziyuan Pu
- Shalmali Joshi
Citations in the corpus
- Recurrent Neural Networks for Multivariate Time Series with Missing Values (2016, background): “However, these solutions often result in a twostep process where imputations are disparate from prediction models and missing patterns are not effectively explored, thus leading to suboptimal analyses and predictions (Wells et al., 2013).”
- Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values (2020, background): “In this way, the missing patterns of the data cannot be effectively explored in prediction models, and thus may result in biased prediction results Wells et al. (2013).”
- Ethical Machine Learning in Healthcare (2020, background): “EHR data are a complex reflection of patient health, health care systems, and providers, where data missingness is a known, and meaningful, problem (61).”