analysis using data from 2014 to 2022. SETTING: Single-center quaternary care PICU. PATIENTS: Two temporally distinct cohorts of PARDS patients, 2014–2019 and 2019–2022. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Patients in the derivation cohort (n = 333) were assigned to hyperinflammatory or hypoinflammatory subphenotypes using biomarkers and latent class analysis. A machine learning model was trained on 165 EHR-derived variables to identify subphenotypes. The most important variables were selected for inclusion in a parsimonious model. The model was validated in a separate cohort (n = 114). The EHR-based classifier achieved an area under the receiver operating characteristic curve (AUC) of 0.93 (95% CI, 0.87–0.98), with a sensitivity of 88% and specificity of 83% for determining hyperinflammatory PARDS. The parsimonious model, using only five laboratory values, achieved an AUC of 0.92 (95% CI, 0.86–0.98) with a sensitivity of 76% and specificity of 87% in the validation coh