使用机器学习和贝叶斯网络算法诊断性传播感染的症状检查器的准确性
Nyi Nyi Soe1,2, Janet M Towns3,4, Phyu Mon Latt3,4
1Melbourne Sexual Health Centre, Alfred Health, 580 Swanston Street, Carlton, Melbourne, VIC, 3053, Australia. drnyinyisoe1989@gmail.com.
BMC infectious diseases
|December 18, 2024
概括
数字工具可以帮助诊断性传播感染 (STIs). 机器学习模型准确地预测了常见的性传播疾病和无遗传条件,在某些情况下表现优于贝叶斯网络.
科学领域:
- 数字健康数字健康
- 医学诊断 医学诊断 医学诊断
- 机器学习在医疗保健中的应用
背景情况:
- 许多人因为性传播感染 (STI) 症状而推迟或避免医疗保健.
- 现有的数字诊断工具不能完全复制临床评估或涵盖各种性传播感染.
研究的目的:
- 开发和评估机器学习 (ML) 算法,用于诊断性传播感染和性器疾病.
- 将ML模型的诊断准确性与贝叶斯网络进行比较.
主要方法:
- 在性健康中心 (2015-2018) 收集了来自10520名有性传播疾病症状的个体的未来数据 (2015-2018).
- 开发使用贝叶斯网络的在线症状检查器 (iSpySTI.org).
- 培训和评估各种ML算法,使用性别特定的问卷和症状数据.
主要成果:
- 对于大多数男性疾病 (例如,NGU,生殖器) 和女性疾病 (例如,候选症,细菌性阴道炎) 的ML模型显示出高的诊断准确性 (AUC 0.81-0.95).
- 关键预测因素包括尿道泄漏,尿道症状,病变的外观/位置和阴道泄漏特征.
- 对于男性平衡炎和生殖器等特定疾病,ML模型的表现明显优于贝叶斯模型.
结论:
- 无论是ML还是贝叶斯模型,都可以使用患者报告的数据准确诊断常见的性传播感染和无遗传状况.
- 未来的研究应该扩大疾病的范围,并探索结合患者收集的图像,以提高诊断准确度.
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