在人工智能时代预测移植的长期结果
Samarra Badrouchi1,2,3, Mohamed Mongi Bacha4,5,6, Abdulaziz Ahmed7
1Department of Internal Medicine A, Charles Nicolle Hospital, Tunis, Tunisia. samarra.nephro@gmail.com.
Scientific reports
|December 2, 2023
概括
预测移植存活率是复杂的. 人工智能 (AI) 模型,使用诸如高血压等因素,可以准确预测移植的5年存活率,帮助科医生做出治疗决策.
科学领域:
- 腎科和移植醫學 腎科和移植醫學
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 准确预测长期移植 (KT) 存活率对于治疗决策至关重要.
- 由于多因素的复杂性,预测KT结果具有挑战性.
- 人工智能 (AI) 为医疗结果预测提供了有希望的工具.
研究的目的:
- 开发和比较传统和基于人工智能的方法来预测长期移植存活率.
- 通过使用统计和机器学习 (ML) 方法,识别移植存活的关键预测因素.
- 创建一个强大的人工智能模型,用于早期和精确地预测5年移植存活率.
主要方法:
- 分析了407例脏移植,根据移植存活率 (>5年与低存活率) 分成组.
- 传统的统计分析,以确定重要的因素.
- 使用机器学习 (ML) 技术开发了35个预测模型.
- 为表现最好的AI模型选择关键变量.
主要成果:
- 较年轻的捐赠者年龄和甲酸莫菲蒂尔 (MMF) 的使用与改善的移植存活率有关.
- 在KT后3个月,更高的eGFR和更少的再入院预测了更好的结果.
- 早期的并发症,如延迟移植功能 (DGF),急性损伤 (AKI) 和急性排斥 (AR),与生存率低下有关.
- 最好的AI模型实现了89.7%的AUC,确定高血压和红细胞输血史作为关键预测因素.
结论:
- 人工智能模型,特别是那些包含高血压和输血史的模型,提供了对5年移植存活率的强大而精确的预测.
- 开发的人工智能模型可以作为脏病学家的有价值的决策支持工具,可以早期检测移植状况.
- 利用易于收集的早期变量,人工智能增强了对移植中长期移植存活率的预测.
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