Length of Stay in the Neonatal ICU is Predictable using Heart Rate: An Opportunity for Optimizing Managed Care.

ABSTRACT

We explore the use of classification and regression models for predicting the length of stay (LoS) of neonatal patients in the intensive care unit (ICU), using heart rate (HR) time-series data of 7,758 patients from the MIMIC-IH database. We find that aggregated features of HR on the first full-day of in-patient stay after admission (i.e. the first day with a full 24-hour record for each patient) can be leveraged to classify LoS in excess of 10 days with 89% sensitivity and 59% specificity. As such, LoS as a continuous variable was also found to be statistically significantly correlated to aggregate HR data corresponding to the first full-day after admission.