“Could She/He Walk Out of the Hospital?”: Implementing AI Models for Recovery Prediction and Doctor-Patient Communication in Major Trauma
<b>Background and Objectives</b>: 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-...
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MDPI AG
2025-06-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/13/1582 |
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| author | Li-Chin Cheng Chung-Feng Liu Chin-Choon Yeh |
| author_facet | Li-Chin Cheng Chung-Feng Liu Chin-Choon Yeh |
| author_sort | Li-Chin Cheng |
| collection | DOAJ |
| description | <b>Background and Objectives</b>: 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. <b>Methods</b>: This retrospective analysis considered major trauma patient admitted to Chi Mei Medical Center from 1 January 2010 to 31 December 2019. <b>Results</b>: 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. <b>Conclusions</b>: 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. |
| format | Article |
| id | doaj-art-cba7a3a7c82a43b9aaf3aa1cac2ae4f1 |
| institution | Matheson Library |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-cba7a3a7c82a43b9aaf3aa1cac2ae4f12025-07-11T14:38:00ZengMDPI AGDiagnostics2075-44182025-06-011513158210.3390/diagnostics15131582“Could She/He Walk Out of the Hospital?”: Implementing AI Models for Recovery Prediction and Doctor-Patient Communication in Major TraumaLi-Chin Cheng0Chung-Feng Liu1Chin-Choon Yeh2Division of Colorectal Surgery, Department of Surgery, Chi Mei Medical Center, Tainan 710402, TaiwanDepartment of Medical Research, Chi Mei Medical Center, Tainan 710402, TaiwanDivision of Traumatology, Department of Surgery, Chi Mei Medical Center, Tainan 710402, Taiwan<b>Background and Objectives</b>: 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. <b>Methods</b>: This retrospective analysis considered major trauma patient admitted to Chi Mei Medical Center from 1 January 2010 to 31 December 2019. <b>Results</b>: 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. <b>Conclusions</b>: 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.https://www.mdpi.com/2075-4418/15/13/1582major trauma patientprognosisrecoverymortalityartificial intelligencemachine learning |
| spellingShingle | Li-Chin Cheng Chung-Feng Liu Chin-Choon Yeh “Could She/He Walk Out of the Hospital?”: Implementing AI Models for Recovery Prediction and Doctor-Patient Communication in Major Trauma Diagnostics major trauma patient prognosis recovery mortality artificial intelligence machine learning |
| title | “Could She/He Walk Out of the Hospital?”: Implementing AI Models for Recovery Prediction and Doctor-Patient Communication in Major Trauma |
| title_full | “Could She/He Walk Out of the Hospital?”: Implementing AI Models for Recovery Prediction and Doctor-Patient Communication in Major Trauma |
| title_fullStr | “Could She/He Walk Out of the Hospital?”: Implementing AI Models for Recovery Prediction and Doctor-Patient Communication in Major Trauma |
| title_full_unstemmed | “Could She/He Walk Out of the Hospital?”: Implementing AI Models for Recovery Prediction and Doctor-Patient Communication in Major Trauma |
| title_short | “Could She/He Walk Out of the Hospital?”: Implementing AI Models for Recovery Prediction and Doctor-Patient Communication in Major Trauma |
| title_sort | could she he walk out of the hospital implementing ai models for recovery prediction and doctor patient communication in major trauma |
| topic | major trauma patient prognosis recovery mortality artificial intelligence machine learning |
| url | https://www.mdpi.com/2075-4418/15/13/1582 |
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