机器学习辅助的手术前预测儿科尾炎的严重程度
Aylin Erman1, Julia Ferreira2, Waseem Abu Ashour2
1Department of Computer Science, McGill University, Montreal, QC, Canada.
Journal of pediatric surgery
|January 24, 2025
概括
机器学习模型准确地预测儿童尾炎的严重程度,超过现有的工具. 这有助于个性化治疗和儿童尾炎的资源管理.
科学领域:
- 儿科手术 儿科手术
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 准确的手术前诊断儿童急性尾炎对于有效管理至关重要.
- 预测尾炎的严重程度有助于定制治疗策略和优化资源配置.
研究的目的:
- 评估机器学习 (ML) 算法的有效性,以改善儿童急性尾炎的术前诊断.
- 使用ML模型准确预测尾炎的严重程度.
主要方法:
- 开发了一条ML管道,使用了1980名接受尾切除术的儿童 (2014-2021) 的回顾性数据集.
- 采用了缺失值的归算策略,以及不常见类的上抽样.
- 测试了各种归算,类平衡和分类模型的组合,以预测5种尾炎等级.
主要成果:
- 最好的ML管道实现了70.1%的准确性和0.77AUROC来区分尾炎的严重程度等级.
- 该模型与另一种儿科尾炎严重程度预测工具 (71.4%准确率,0.54AUROC) 相比,表现优越.
- 关键预测变量与临床经验和文献一致.
结论:
- 机器学习模型可以有效地预测儿童尾炎的严重程度,优于现有的工具.
- 开发的ML模型为术前评估尾炎严重程度提供了一种新的方法.
- 在儿科尾炎管理中,有个性化,基于严重程度的治疗和优化资源配置的潜力.
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