  Background: Elderly patients with MODS have high risk of death and poor
prognosis. The performance of current scoring systems assessing the severity of
MODS and its mortality remains unsatisfactory. This study aims to develop an
interpretable and generalizable model for early mortality prediction in elderly
patients with MODS. Methods: The MIMIC-III, eICU-CRD and PLAGH-S databases were
employed for model generation and evaluation. We used the eXtreme Gradient
Boosting model with the SHapley Additive exPlanations method to conduct early
and interpretable predictions of patients' hospital outcome. Three types of
data source combinations and five typical evaluation indexes were adopted to
develop a generalizable model. Findings: The interpretable model, with optimal
performance developed by using MIMIC-III and eICU-CRD datasets, was separately
validated in MIMIC-III, eICU-CRD and PLAGH-S datasets (no overlapping with
training set). The performances of the model in predicting hospital mortality
as validated by the three datasets were: AUC of 0.858, sensitivity of 0.834 and
specificity of 0.705; AUC of 0.849, sensitivity of 0.763 and specificity of
0.784; and AUC of 0.838, sensitivity of 0.882 and specificity of 0.691,
respectively. Comparisons of AUC between this model and baseline models with
MIMIC-III dataset validation showed superior performances of this model; In
addition, comparisons in AUC between this model and commonly used clinical
scores showed significantly better performance of this model. Interpretation:
The interpretable machine learning model developed in this study using fused
datasets with large sample sizes was robust and generalizable. This model
outperformed the baseline models and several clinical scores for early
prediction of mortality in elderly ICU patients. The interpretative nature of
this model provided clinicians with the ranking of mortality risk features.
