Our research is focused on Post ICU pain syndromes (PIPS). PIPS exhibit distinct phenotypic presentations and can be predicted by intra-ICU parameters. Our primary goal is to be able to predict post-ICU opioid use based on intra-ICU parameters. We utilize a data-driven characterization of post-ICU pain syndromes will utilize unsupervised clustering algorithms including DBSCAN and spectral clustering. Prediction of post-discharge pain severity, likelihood of specific pain presentations, and post-discharge opioid use will be achieved using logistic LASSO, random forests, and neural networks. Specifically, these tests will utilize available ICU data to predict changes between pre-
and post-ICU pain severity, incidence of specific pain presentations, and incidence of opioid use.
