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Aims <p>Comorbidity is highly prevalent in the elderly in China, representing a leading cause of mortality in this population. This study established a multicenter dataset specific to geriatric comorbidities and explored the performance in early warning of in-hospital adverse events using multiple machine learning models.</p> Methods and results <p>Data were collected in the elderly from northern, central, and southern regions of China. Following data processing, a dataset specific to geriatric comorbidities was established. Among the patients, over 90% had at least one geriatric syndrome. Machine learning methods were applied to predict adverse events during hospitalization, including Random Forest, Support Vector Machine (SVM), 1-Dimensional Convolutional Neural Network (1D CNN), Gradient Boosting Decision Tree (GBDT), and eXtreme Gradient Boosting (XGBoost). GBDT (AUROC = 0.91, ACC = 0.903, REC = 0.878, PRE = 0.808, F1 = 0.836) and XGBoost (AUROC = 0.914, ACC = 0.91, REC = 0.893, PRE = 0.817, F1 = 0.848) demonstrated better prediction performance. Shapley Additive Explanation (SHAP) method was used to identify features significantly associated with the occurrence of adverse events and presented the top ten features based on their significance.</p> Conclusions <p>A geriatric comorbidity-specific dataset was established. XGBoost demonstrated the better performance in predicting risk of in-hospital adverse events. Frailty, D-dimer, disease severity grade, Barthel Index, and fibrin degradation products were significantly associated with the occurrence of such events.</p>

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Establishment of the China Elderly Comorbidity Medical Database (CECMed) and its application in machine learning-based prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月19日

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  • Jingwen Shi,
  • Duanchang Wan,
  • Wen Tang,
  • Xuedong Wang,
  • Longyu Li,
  • Wei Chen,
  • Bing Liu,
  • Xuebing Yang,
  • Ying Sun

摘要

Aims

Comorbidity is highly prevalent in the elderly in China, representing a leading cause of mortality in this population. This study established a multicenter dataset specific to geriatric comorbidities and explored the performance in early warning of in-hospital adverse events using multiple machine learning models.

Methods and results

Data were collected in the elderly from northern, central, and southern regions of China. Following data processing, a dataset specific to geriatric comorbidities was established. Among the patients, over 90% had at least one geriatric syndrome. Machine learning methods were applied to predict adverse events during hospitalization, including Random Forest, Support Vector Machine (SVM), 1-Dimensional Convolutional Neural Network (1D CNN), Gradient Boosting Decision Tree (GBDT), and eXtreme Gradient Boosting (XGBoost). GBDT (AUROC = 0.91, ACC = 0.903, REC = 0.878, PRE = 0.808, F1 = 0.836) and XGBoost (AUROC = 0.914, ACC = 0.91, REC = 0.893, PRE = 0.817, F1 = 0.848) demonstrated better prediction performance. Shapley Additive Explanation (SHAP) method was used to identify features significantly associated with the occurrence of adverse events and presented the top ten features based on their significance.

Conclusions

A geriatric comorbidity-specific dataset was established. XGBoost demonstrated the better performance in predicting risk of in-hospital adverse events. Frailty, D-dimer, disease severity grade, Barthel Index, and fibrin degradation products were significantly associated with the occurrence of such events.