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. 2025 Jun 22;15(13):1582.
doi: 10.3390/diagnostics15131582.

"Could She/He Walk Out of the Hospital?": Implementing AI Models for Recovery Prediction and Doctor-Patient Communication in Major Trauma

Li-Chin Cheng  1   2 , Chung-Feng Liu  3 , Chin-Choon Yeh  2   4   5   6
Affiliations

"Could She/He Walk Out of the Hospital?": Implementing AI Models for Recovery Prediction and Doctor-Patient Communication in Major Trauma

Li-Chin Cheng et al. Diagnostics (Basel). .

Abstract

Background and Objectives: Major trauma ranks among the leading causes of mortality and handicap in both developing and developed countries, consuming substantial healthcare resources. Its unpredictable nature and diverse clinical presentations often lead to rapid and challenging-to-predict changes in patient conditions. An increasing number of models have been developed to address this challenge. Given our access to extensive and relatively comprehensive data, we seek assistance in making a meaningful contribution to this topic. This study aims to leverage artificial intelligence (AI)/machine learning (ML) to forecast potential adverse effects in major trauma patients. Methods: This retrospective analysis considered major trauma patient admitted to Chi Mei Medical Center from 1 January 2010 to 31 December 2019. Results: A total of 5521 major trauma patients were analyzed. Among five AI models tested, XGBoost showed the best performance (AUC 0.748), outperforming traditional clinical scores such as ISS and GCS. The model was deployed as a web-based application integrated into the hospital information system. Preliminary clinical use demonstrated improved efficiency, interpretability through SHAP analysis, and positive user feedback from healthcare professionals. Conclusions: This study presents a predictive model for estimating recovery probabilities in severe burn patients, effectively integrated into the hospital information system (HIS) without complex computations. Clinical use has shown improved efficiency and quality. Future efforts will expand predictions to include complications and treatment outcomes, aiming for broader applications as technology advances.

Keywords: artificial intelligence; hospital information systems; machine learning; major trauma patient; mortality; prognosis; recovery.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Research flow.
Figure 2
Figure 2
SHAP plot for the best model of recovery (XGBoost).
Figure 3
Figure 3
Two snapshots of the AI prediction system with the best model.
Figure 3
Figure 3
Two snapshots of the AI prediction system with the best model.
See this image and copyright information in PMC

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