  There is growing interest in applying machine learning methods to Electronic
Medical Records (EMR). Across different institutions, however, EMR quality can
vary widely. This work investigated the impact of this disparity on the
performance of three advanced machine learning algorithms: logistic regression,
multilayer perceptron, and recurrent neural network. The EMR disparity was
emulated using different permutations of the EMR collected at Children's
Hospital Los Angeles (CHLA) Pediatric Intensive Care Unit (PICU) and
Cardiothoracic Intensive Care Unit (CTICU). The algorithms were trained using
patients from the PICU to predict in-ICU mortality for patients in a held out
set of PICU and CTICU patients. The disparate patient populations between the
PICU and CTICU provide an estimate of generalization errors across different
ICUs. We quantified and evaluated the generalization of these algorithms on
varying EMR size, input types, and fidelity of data.
