机器学习在急性胰腺炎的临床应用中的进展:一篇综述
Zhaowang Tan1, Gaoxiang Li1, Yueliang Zheng1
1Emergency and Critical Care Center, Department of Emergency Medicine, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang, China.
Frontiers in medicine
|January 22, 2025
概括
机器学习 (ML) 与传统方法相比,可以更好地预测急性胰腺炎 (AP) 的结果. 本综述强调了最近的ML应用在评估AP严重程度,并发症和治疗时间方面.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
背景情况:
- 对于急性胰腺炎 (AP) 的传统评分系统通常在预测疾病进展和预后方面缺乏准确性.
- 现有的模型很难为AP患者提供简洁可靠的预后信息.
研究的目的:
- 审查最近应用机器学习 (ML) 模型对急性胰腺炎 (AP) 的进展.
- 探索ML在各种AP方面的实用性,包括严重程度,并发症和手术时间.
主要方法:
- 关于在AP中ML应用的最近研究的文献综述.
- 在预测AP结果时分析ML模型的性能.
主要成果:
- ML模型在提高AP严重程度和并发症的预测方面表现有前途.
- 新兴的人工智能技术正在被用于优化AP的管理决策.
结论:
- 机器学习代表了与AP预测传统方法相比的重大进步.
- 对ML应用的进一步研究可以改善患者的治疗结果和AP中的临床决策.
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