在急诊室准确诊断急性尾炎:基于人工智能的方法
Ghodratollah Roshanaei1, Rasoul Salimi2, Hossein Mahjub1
1Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran.
Internal and emergency medicine
|August 21, 2024
概括
机器学习模型在急诊室准确诊断尾炎,改进了像阿尔瓦拉多分数这样的传统方法. 高斯的天真贝叶斯表现出最高的准确性,有助于更快,更精确的病人护理.
科学领域:
- 医疗信息学 医疗信息学
- 紧急医疗 紧急医疗
- 机器学习 机器学习
背景情况:
- 在急诊室诊断腹痛是一项挑战,尾炎是常见的担忧.
- 非典型尾炎的症状可能会推迟诊断,而阿尔瓦拉多得分在特异性上有局限性,可能导致不必要的手术.
研究的目的:
- 通过机器学习,提高急诊部门尾炎的诊断准确度.
- 为了区分尾炎与其他导致急性腹痛的原因.
主要方法:
- 收集了来自534名急性腹痛患者的数据.
- 使用的机器学习算法:支持矢量分类器,随机森林分类器,梯度增强分类器和高斯素朴贝叶斯.
- 使用Brier分数进行评估模型校准.
主要成果:
- 高斯的天真贝叶斯模型实现了最高的精度 (95.03%),灵敏度 (87.18%) 和特异性 (97.54%).
- 其他模型也表现出高性能:随机森林 (准确率92.55%),梯度提升 (准确率94.41%) 和支持向量分类器 (准确率91.93%).
- 胆囊炎 (37.07%) 和尾炎 (25.84%) 是最常见的诊断.
结论:
- 机器学习方法显著提高了尾炎诊断在紧急情况下的准确性.
- 这些先进的算法可以帮助临床医生做出更精确的诊断决策,从而减少误诊和不必要的手术.
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