Machine learning as a tool predicting short-term postoperative complications in Crohn's disease patients undergoing intestinal resection: What frontiers?

IF 1.8 4区 医学 Q3 GASTROENTEROLOGY & HEPATOLOGY
Raffaele Pellegrino, Antonietta Gerarda Gravina
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引用次数: 0

Abstract

The recent study, "Predicting short-term major postoperative complications in intestinal resection for Crohn's disease: A machine learning-based study" investigated the predictive efficacy of a machine learning model for major postoperative complications within 30 days of surgery in Crohn's disease (CD) patients. Employing a random forest analysis and Shapley Additive Explanations, the study prioritizes factors such as preoperative nutritional status, operative time, and CD activity index. Despite the retrospective design's limitations, the model's robustness, with area under the curve values surpassing 0.8, highlights its clinical potential. The findings align with literature supporting preoperative nutritional therapy in inflammatory bowel diseases, emphasizing the importance of comprehensive assessment and optimization. While a significant advancement, further research is crucial for refining preoperative strategies in CD patients.

将机器学习作为预测接受肠切除术的克罗恩病患者术后短期并发症的工具:前沿在哪里?
最近一项名为 "预测克罗恩病肠切除术后短期主要并发症:基于机器学习的研究 "调查了机器学习模型对克罗恩病(CD)患者手术后 30 天内主要术后并发症的预测效果。该研究采用随机森林分析法和夏普利相加解释法,优先考虑术前营养状况、手术时间和克罗恩病活动指数等因素。尽管回顾性设计存在局限性,但该模型的稳健性(曲线下面积值超过 0.8)凸显了其临床潜力。研究结果与支持炎症性肠病术前营养治疗的文献一致,强调了全面评估和优化的重要性。虽然这是一项重大进展,但进一步的研究对于完善 CD 患者的术前策略至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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