English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Background: Elderly patients with MODS have high risk of death and poorprognosis. The performance of current scoring systems assessing the severity ofMODS and its mortality remains unsatisfactory. This study aims to develop aninterpretable and generalizable model for early mortality prediction in elderlypatients with MODS. Methods: The MIMIC-III, eICU-CRD and PLAGH-S databases wereemployed for model generation and evaluation. We used the eXtreme GradientBoosting model with the SHapley Additive exPlanations method to conduct earlyand interpretable predictions of patients' hospital outcome. Three types ofdata source combinations and five typical evaluation indexes were adopted todevelop a generalizable model. Findings: The interpretable model, with optimalperformance developed by using MIMIC-III and eICU-CRD datasets, was separatelyvalidated in MIMIC-III, eICU-CRD and PLAGH-S datasets (no overlapping withtraining set). The performances of the model in predicting hospital mortalityas validated by the three datasets were: AUC of 0.858, sensitivity of 0.834 andspecificity of 0.705; AUC of 0.849, sensitivity of 0.763 and specificity of0.784; and AUC of 0.838, sensitivity of 0.882 and specificity of 0.691,respectively. Comparisons of AUC between this model and baseline models withMIMIC-III dataset validation showed superior performances of this model; Inaddition, comparisons in AUC between this model and commonly used clinicalscores showed significantly better performance of this model. Interpretation:The interpretable machine learning model developed in this study using fuseddatasets with large sample sizes was robust and generalizable. This modeloutperformed the baseline models and several clinical scores for earlyprediction of mortality in elderly ICU patients. The interpretative nature ofthis model provided clinicians with the ranking of mortality risk features.

Interpretable Machine Learning Model for Early Prediction of Mortality  in Elderly Patients with Multiple Organ Dysfunction Syndrome (MODS): a  Multicenter Retrospective Study and Cross Validation