开发和验证一种诊断模型,通过结合多个机器学习算法来区分脊髓结核与发热性脊髓炎
Chengqian Huang1, Jing Zhuo2, Chong Liu1
1Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Biomolecules & biomedicine
|October 28, 2023
概括
这项研究开发了一种机器学习模型,以区分脊髓结核病 (STB) 和发热性脊髓炎 (PS). 该模型使用关键的血液标志物准确区分这些疾病,提高脊髓感染的诊断速度和精度.
科学领域:
- 整形外科和脊椎外科手术
- 传染性疾病 传染性疾病
- 医学诊断 医学诊断 医学诊断
- 机器学习在医学中的应用
背景情况:
- 脊柱结核 (STB) 和炎症性脊柱炎 (PS) 是不同的脊柱感染,临床表现重叠.
- 准确区分STB和PS对于适当的治疗和改善患者结果至关重要.
- 目前的诊断方法可能会耗时,并且可能并不总能提供明确的差异化.
研究的目的:
- 开发和验证用于区分STB和PS的诊断模型.
- 通过机器学习识别主要的临床和实验室变量,以区分STB和PS.
- 为医疗保健从业人员创建可靠的工具,以帮助快速准确地诊断脊髓感染.
主要方法:
- 对387个确诊的STB (n=241) 和PS (n=146) 病例进行了回顾性分析.
- 应用四种机器学习算法 (LASSO,逻辑回归,随机森林,SVM-RFE) 来识别训练组中的独特变量 (n=271).
- 使用已识别的变量构建和验证诊断模型,通过ROC曲线,校准曲线和验证组 (n=116) 进行评估.
主要成果:
- 通过机器学习算法确定了七个关键变量,以形成诊断模型.
- 该模型在培训组实现了0.841的曲线下面积 (AUC),在验证组达到0.83.
- 在STB和PS患者之间,在血小板与中性粒细胞比率 (PNR),中性粒细胞与淋巴细胞比率 (NLR),血小板体积分布宽度 (PDW),平均血小板体积 (MPV),血红蛋白 (HGB) 和红细胞 (RBC) 计数方面观察到显著差异.
结论:
- 开发的基于机器学习的诊断模型在区分STB和PS时表现出高准确度.
- 该模型有效地利用现有的实验室参数来支持临床决策.
- 这种工具可以促进更快,更精确的诊断,从而及时和适当地管理脊髓感染.
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