Mining for Equitable Health: Assessing the Impact of Missing Data in Electronic Health Records

Electronic health records (EHRs) are collected as a routine part of healthcare delivery, and have great potential to be utilized to improve patient health outcomes. They contain multiple years of health information to be leveraged for risk prediction, disease detection, and treatment evaluation. However, they do not have standardized formatting, and can present significant analytical challenges– they contain multi-scale data from heterogeneous domains and include both structured and unstructured data. Data for individual patients are collected at irregular time intervals and with varying frequencies. In addition to the analytical challenges, EHRs can reflect inequity– patients belonging to different groups will have differing amounts of data in their health records. Many of these issues can contribute to biased data collection. The consequence is that the data for marginalized groups may be less informative due to more fragmented care, which can be viewed as a type of missing data problem. For EHRs data in this complex form, there is currently no framework for introducing missing values. There has also been little to no work in assessing the impact of missing data in EHRs. In this work, we simulate realistic missing data scenarios in EHRs to adequately assess their impact on predictive modeling. We incorporate the use of a medical knowledge graph to capture dependencies between medical events to create a more realistic missing data framework. In an intensive care unit setting, we found that missing data have greater negative impact on the performance of disease prediction models in groups that tend to have less access to healthcare, or seek less healthcare. We also found that the impact of missing data on disease prediction models is stronger when using the knowledge graph framework to introduce realistic missing values as opposed to random event removal.