![]() We compared PICTURE with existing early warning systems and logistic regression at multiple levels of granularity. We analyzed primary outcomes of unplanned intensive care unit transfer, emergency vasoactive medication administration, cardiac arrest, and death. To develop, tune, and test PICTURE, we used labs and vital signs from electronic health records of adult patients over four years (n = 133,089 encounters). ![]() We used this information to build a novel early warning system for predicting patient deterioration in general wards and telemetry units: PICTURE ( Predicting Intensive Care Transfers and other Unfo Reseen Events). We found that randomized and Bayesian regression imputation methods mitigate the issue of learning the missingness pattern for tree-based methods. Performance is worst in this scenario when the missing rate between those with and without an adverse event is the greatest. We showed that when tree-based methods learn missingness patterns (correlated with adverse events) in electronic health record data, this leads to decreased performance if the system is used in a new setting that has different missingness patterns. ![]() To demonstrate this, we developed a novel simulation that generated synthetic electronic health record data using a variational autoencoder with a custom loss function, which took into account the high missing rate of electronic health data. When using tree-based methods to develop predictive analytics and early warning systems for preventive healthcare, it is important to use an appropriate imputation method to prevent learning the missingness pattern.
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