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Esophageal Perforation-II: Clinical Manifestations and Management01:28

Esophageal Perforation-II: Clinical Manifestations and Management

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Esophageal perforations manifest in various clinical forms, influenced by factors such as the perforation's cause and location (cervical, intrathoracic, or intra-abdominal), the extent of contamination, and potential injury to adjacent mediastinal structures. The timing between the perforation occurrence and treatment initiation also affects the clinical presentation.
Clinical Manifestations:
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用机器学习来预测食道切除术后的并发症.

Jorn-Jan van de Beld1,2, David Crull2, Julia Mikhal2,3

  • 1Faculty of EEMCS, University of Twente, 7500 AE Enschede, The Netherlands.

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|February 24, 2024
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概括

机器学习准确地预测了食道切除术 (食道癌手术) 后的解剖学泄漏和肺炎. 这些人工智能模型提供了早期警告,改善了患者的治疗结果和手术护理.

关键词:
临床决策支持 临床决策支持消化管切除术是指消化管切除术.多模式机器学习时间学习时间学习.

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科学领域:

  • 医学技术 医学技术 医学技术
  • 医学中的人工智能
  • 手术瘤学手术瘤学

背景情况:

  • 消化管切除术是消化管癌的关键治疗方法,但存在高风险的术后并发症.
  • 在食道切除术后,解剖管泄漏和肺炎是严重的担忧,影响患者的康复和结果.

研究的目的:

  • 为了评估机器学习 (ML) 模型在预测静脉漏和肺炎方面的有效性.
  • 评估ML对这些并发症的预测性能,提前两天.

主要方法:

  • 利用了2011年至2021年间接受食道切除术的417名患者的数据集.
  • 集成的多模式时间数据,包括实验室结果,生命体征,胸部图像和手术前患者特征.
  • 开发并验证了ML模型,以预测静脉漏和肺炎.

主要成果:

  • 最好的ML模型实现了0.87的AUROC和0.82的AUROC,分别预测了1天和2天的前途.
  • 对于肺炎预测,模型在预测前1天和2天的时间分别达到0.74和0.61的AUROC.
  • 对这两种并发症都表现出强大的预测能力.

结论:

  • 机器学习模型可以有效地预测输卵管切除术后的门泄漏和肺炎.
  • 使用ML早期预测这些并发症可能会改善患者的管理和结果.
  • 突出了人工智能的潜力,以提高食道癌症手术中的术后护理.