Predicting in-hospital length of stay: a two-stage modeling approach to account for highly skewed data.

ABSTRACT:

Background

In the early stages of the COVID-19 pandemic our institution was interested in forecasting how long surgical patients receiving elective procedures would spend in the hospital. Initial examination of our models indicated that, due to the skewed nature of the length of stay, accurate prediction was challenging and we instead opted for a simpler classification model. In this work we perform a deeper examination of predicting in-hospital length of stay.

Methods

We used electronic health record data on length of stay from 42,209 elective surgeries. We compare different loss-functions (mean squared error, mean absolute error, mean relative error), algorithms (LASSO, Random Forests, multilayer perceptron) and data transformations (log and truncation). We also assess the

SUBMITTER: Xu Z 

PROVIDER: S-EPMC9035272 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature