Clinical presentation of COVID-19 - a model derived by a machine learning algorithm.
Clinical presentation of COVID-19 - a model derived by a machine learning algorithm.
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DOI：
10.1515/jib-2020-0050
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发表时间：
2021-03-04
影响因子：
1.9
通讯作者：
Ben Shlomo I
中科院分区：
文献类型：
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作者：
Yousef M;Showe LC;Ben Shlomo I
COVID-19大流行已经淹没了所有的分诊站，很难仔细选择那些最有可能被感染的人。检测、感染和住院患者总数的数据是零碎的，因此很难轻易地选择那些最有可能被感染的人。以色列卫生部公布了截至2020年4月18日进行的所有病毒DNA检测（包括近12万次检测）的即时临床数据和各自感染/未感染状况的登记册。我们使用机器学习算法来找出哪些直接的临床因素在确定测试者的真实状态（包括年龄或性别问题）方面最重要，以便将来更好地为属于高风险群体的人分配监测政策。除了对第一批可用数据（4月11日）进行分析外，我们还对独立的第二批数据（4月12日至18日）进行了进一步的算法测试。发烧、咳嗽和头痛是最具诊断性的，在不同的亚组中重要性不同。男性的阳性比例较高（9.3比7.3%），但性别与临床表现无关。该模型预测精度为0.84，曲线下面积为0.92，预测能力强。我们提供一份手持式简短检查表，对主要症状的重要性进行口头描述，这将加快分诊，并使正确选择患者进行进一步随访。
COVID-19 pandemic has flooded all triage stations, making it difficult to carefully select those most likely infected. Data on total patients tested, infected, and hospitalized is fragmentary making it difficult to easily select those most likely to be infected. The Israeli Ministry of Health made public its registry of immediate clinical data and the respective status of infected/not infected for all viral DNA tests performed up to Apr. 18th, 2020 including almost 120,000 tests. We used a machine-learning algorithm to find out which immediate clinical elements mattered the most in identifying the true status of the tested persons including age or gender matter, to enable future better allocation of surveillance policy for those belonging to high-risk groups. In addition to the analyses applied on the first batch of the available data (Apr. 11th), we further tested the algorithm on the independent second batch (Apr. 12th to 18th). Fever, cough and headache were the most diagnostic, differing in degree of importance in different subgroups. Higher percentage of men were found positive (9.3 vs. 7.3%), but gender did not matter for the clinical presentation. The prediction power of the model was high, with accuracy of 0.84 and area under the curve 0.92. We provide a hand-held short checklist with verbal description of importance for the leading symptoms, which should expedite the triage and enable proper selection of people for further follow-up.