• Gregory Boverman and Sahika Genc.
• Conf Proc IEEE Eng Med Biol Soc. 2014 Jan 1; 2014: 3464-7.

AbstractWith the advent of inexpensive storage, pervasive networking, and wireless devices, it is now possible to store a large proportion of the medical data that is collected in the intensive care unit (ICU). These data sets can be used as valuable resources for developing and validating predictive analytics. In this report, we focus on the problem of prediction of mortality from respiratory distress among long-term mechanically ventilated patients using data from the publicly-available MIMIC-II database. Rather than only reporting p-values for univariate or multivariate regression, as in previous work, we seek to generate sparsest possible model that will predict mortality. We find that the presence of severe sepsis is highly associated with mortality. We also find that variables related to respiration rate have more predictive accuracy than variables related to oxygenation status. Ultimately, we have developed a model which predicts mortality from respiratory distress in the ICU with a cross-validated area-under-the-curve (AUC) of approximately 0.74. Four methodologies are utilized for model dimensionality-reduction: univariate logistic regression, multivariate logistic regression, decision trees, and penalized logistic regression.

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