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机器学习的基于症状的评分技术来预测COVID-19:一个验证研究

Amelia Nur Vidyanti^1,2, Sekar Satiti^1,2, Atitya Fithri Khairani^1,2

¹Department of Neurology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia.

BMC infectious diseases

|December 13, 2023

概括

一个新的基于症状的评分系统准确地预测了医疗保健环境中的COVID-19,显示了高精度和灵敏度. 需要进一步的研究来证实其对未来激增期间平衡检测和工作负载的影响.

科学领域:

传染性疾病传染性疾病
公共卫生公共卫生
医疗保健中的机器学习

背景情况:

在COVID-19爆发期间,医疗保健系统面临着挑战,需要改进诊断预测,以有效管理患者.
为一般人群开发了一种基于机器学习的症状评分系统,但需要在临床环境中进行验证.
嗅觉和味觉丧失等关键症状被纳入预测模型中的主要指标.

研究的目的:

验证基于机器学习的症状评分系统,用于在医院环境中预测COVID-19.
评估系统在识别COVID-19病例方面的精度和灵敏度.
评估评分系统在改善患者转诊和管理医疗保健工作负载方面的潜力.

主要方法:

一项横截面研究分析了Sardjito医院 (2020年3月至2021年12月) 的患者记录.
逆转录聚合酶链反应 (RT-PCR) 用于结果确认.
基于症状的评分系统 (指数测试) 与抗原测试,抗体测试和医生的临床判断进行了比较,评估了积极预测值 (PPV) 和灵敏度.

主要成果:

临床判断具有61%的PPV.
基于症状的评分系统实现了高PPV的85%,但敏感度低 (17%).
将评分系统与抗原测试相结合,PPV显著提高到92%和灵敏度达到88%,超过单独的抗原测试 (71%的灵敏度).

结论:

经验证的基于症状的COVID-19预测得分证明了医疗机构的准确性和敏感性.
该系统有望提高诊断准确度,并有可能平衡医疗检测和工作量.
建议进行一项影响研究,以确认该系统在管理未来COVID-19爆发时的有效性.

关键词:

在 COVID-19 疫情中,临床预测规则临床预测规则医院推医院推机器学习是机器学习.验证研究的验证研究.

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