Public articles linked to the same research event.
Journal of Medical Internet Research Using electronic health record data from 9,496 patients with trauma treated in the Capital Region of Denmark between 2017 and 2024, this study developed a hybrid neural network combining tabular and sequential data to predict 30-day all-cause mortality at any time point from prehospital care to discharge, achieving AUROC 0.962 and AUPRC 0.655 on a holdout set of 1,829 patients and AUROC 0.905 at 1 hour from first patient contact in active-cohort evaluation, with better discrimination than the Revised Trauma Score (mean ΔAUROC +0.297) and the Trauma and Injury Severity Score (mean ΔAUROC +0.169).
Using electronic health record data from 9,496 patients with trauma treated in the Capital Region of Denmark between 2017 and 2024, this study developed a hybrid neural network combining tabular and sequential data to predict 30-day all-cause mortality at any time point from prehospital care to discharge, achieving AUROC 0.962 and AUPRC 0.655 on a holdout set of 1,829 patients and AUROC 0.905 at 1 hour from first patient contact in active-cohort evaluation, with better discrimination than the Revised Trauma Score (mean ΔAUROC +0.297) and the Trauma and Injury Severity Score (mean ΔAUROC +0.169).
Using electronic health record data from 9,496 patients with trauma treated in the Capital Region of Denmark between 2017 and 2024, this study developed a hybrid neural network combining tabular and sequential data to predict 30-day all-cause mortality at any time point from prehospital care to discharge, achieving AUROC 0.962 and AUPRC 0.655 on a holdout set of 1,829 patients and AUROC 0.905 at 1 hour from first patient contact in active-cohort evaluation, with better discrimination than the Revised Trauma Score (mean ΔAUROC +0.297) and the Trauma and Injury Severity Score (mean ΔAUROC +0.169).
Using electronic health record data from 9,496 patients with trauma treated in the Capital Region of Denmark between 2017 and 2024, this study developed a hybrid neural network combining tabular and sequential data to predict 30-day all-cause mortality at any time point from prehospital care to discharge, achieving AUROC 0.962 and AUPRC 0.655 on a holdout set of 1,829 patients and AUROC 0.905 at 1 hour from first patient contact in active-cohort evaluation, with better discrimination than the Revised Trauma Score (mean ΔAUROC +0.297) and the Trauma and Injury Severity Score (mean ΔAUROC +0.169).