Public articles linked to the same research event.
Anesthesiology Using 203,434 ICU admissions from 2001 to 2022 across more than 200 hospitals in the MIMIC-III, MIMIC-IV, eICU, and HiRID databases, the study developed and externally validated a multimodal deep-learning model that predicts subsequent inpatient mortality from time-invariant variables, time-variant variables, clinical notes, and chest x-ray images available within the first 24 h of ICU admission; with structured data alone the model reached an AUROC of 0.92 (95% CI, 0.90 to 0.93), an AUPRC of 0.53 (95% CI, 0.49 to 0.57), and a Brier score of 0.19 (95% CI, 0.18 to 0.20), external validation across eight eICU institutions yielded AUROCs of 0.84 to 0.92, and in the subgroup with both notes and imaging, adding text and images raised the AUROC modestly from 0.87 (95% CI, 0.85 to 0.89) to 0.
Using 203,434 ICU admissions from 2001 to 2022 across more than 200 hospitals in the MIMIC-III, MIMIC-IV, eICU, and HiRID databases, the study developed and externally validated a multimodal deep-learning model that predicts subsequent inpatient mortality from time-invariant variables, time-variant variables, clinical notes, and chest x-ray images available within the first 24 h of ICU admission; with structured data alone the model reached an AUROC of 0.92 (95% CI, 0.90 to 0.93), an AUPRC of 0.53 (95% CI, 0.49 to 0.57), and a Brier score of 0.19 (95% CI, 0.18 to 0.20), external validation across eight eICU institutions yielded AUROCs of 0.84 to 0.92, and in the subgroup with both notes and imaging, adding text and images raised the AUROC modestly from 0.87 (95% CI, 0.85 to 0.89) to 0.
Using 203,434 ICU admissions from 2001 to 2022 across more than 200 hospitals in the MIMIC-III, MIMIC-IV, eICU, and HiRID databases, the study developed and externally validated a multimodal deep-learning model that predicts subsequent inpatient mortality from time-invariant variables, time-variant variables, clinical notes, and chest x-ray images available within the first 24 h of ICU admission; with structured data alone the model reached an AUROC of 0.92 (95% CI, 0.90 to 0.93), an AUPRC of 0.53 (95% CI, 0.49 to 0.57), and a Brier score of 0.19 (95% CI, 0.18 to 0.20), external validation across eight eICU institutions yielded AUROCs of 0.84 to 0.92, and in the subgroup with both notes and imaging, adding text and images raised the AUROC modestly from 0.87 (95% CI, 0.85 to 0.89) to 0.
Using 203,434 ICU admissions from 2001 to 2022 across more than 200 hospitals in the MIMIC-III, MIMIC-IV, eICU, and HiRID databases, the study developed and externally validated a multimodal deep-learning model that predicts subsequent inpatient mortality from time-invariant variables, time-variant variables, clinical notes, and chest x-ray images available within the first 24 h of ICU admission; with structured data alone the model reached an AUROC of 0.92 (95% CI, 0.90 to 0.93), an AUPRC of 0.53 (95% CI, 0.49 to 0.57), and a Brier score of 0.19 (95% CI, 0.18 to 0.20), external validation across eight eICU institutions yielded AUROCs of 0.84 to 0.92, and in the subgroup with both notes and imaging, adding text and images raised the AUROC modestly from 0.87 (95% CI, 0.85 to 0.89) to 0.