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There is growing interest in applying machine learning methods to ElectronicMedical Records (EMR). Across different institutions, however, EMR quality canvary widely. This work investigated the impact of this disparity on theperformance of three advanced machine learning algorithms: logistic regression,multilayer perceptron, and recurrent neural network. The EMR disparity wasemulated using different permutations of the EMR collected at Children'sHospital Los Angeles (CHLA) Pediatric Intensive Care Unit (PICU) andCardiothoracic Intensive Care Unit (CTICU). The algorithms were trained usingpatients from the PICU to predict in-ICU mortality for patients in a held outset of PICU and CTICU patients. The disparate patient populations between thePICU and CTICU provide an estimate of generalization errors across differentICUs. We quantified and evaluated the generalization of these algorithms onvarying EMR size, input types, and fidelity of data.

The Dependence of Machine Learning on Electronic Medical Record Quality