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基于机器学习的决策支持系统在推疑似风湿性疾病时的实用性
Hakan Babaoğlu1, Hasan Satiş2, Yasin Kavak3
1Rheumatology Clinic, University of Health Sciences Ankara Bilkent City Hospital, Ankara, Turkey. hakanbabaoglu@gmail.com.
Clinical and experimental rheumatology
|October 3, 2025
概括
一个新的机器学习 (ML) 工具准确地识别了需要类风湿病转诊的患者,减少了不必要的预约. 该系统通过更好地管理患者流量,有助于提高类风湿病诊所的效率.
科学领域:
- 类风湿病学 类风湿病学
- 人工智能的人工智能
- 医疗保健信息学 医疗保健信息学
背景情况:
- 风湿性疾病 (RD) 的流行率上升带来了诊断挑战.
- 风湿病学家的短缺影响了及时的病人护理.
- 需要有效的转诊系统来管理患者流量和资源.
研究的目的:
- 开发和评估基于自适应机器学习 (ML) 的决策支持系统.
- 促进疑似风湿性疾病患者的准确转诊.
- 提高诊断准确度,减少不必要的咨询.
主要方法:
- 基于网络的调查收集患者症状数据.
- 六个月的随访以确定类风湿病的状态.
- 五倍交叉验证以评估ML模型的性能 (灵敏度,特异性,AUC).
主要成果:
- 在ML模型中,平均AUC达到77.9%,灵敏度为87.1%,特异性为67.8%.
- 性能最好的模型的AUC为81.34%,灵敏度为81.74%,特异性为80.95%.
- 添加四个特定问题显著提高了性能 (AUC 90.77%,灵敏度 89.74%,特异性 92.05%).
结论:
- 基于ML的分类工具显示了准确推识别的潜力.
- 该系统可以减少不必要的咨询,并提高风湿病学资源利用率.
- 建议进行代改进和未来的多中心验证以实现更广泛的实施.
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