新型冠状病毒感染肺炎患者辅助诊断预测模型的建立 |
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引用本文: | 张冬梅,席莉莉,张小龙,谢俊强,赵旭,王治中,高燕,魏玉辉,于海涛,席亚明. 新型冠状病毒感染肺炎患者辅助诊断预测模型的建立[J]. 医学检验与临床, 2021, 32(3): 1-5 |
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作者姓名: | 张冬梅 席莉莉 张小龙 谢俊强 赵旭 王治中 高燕 魏玉辉 于海涛 席亚明 |
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作者单位: | 兰州大学第一医院药剂科,甘肃兰州 730000;兰州大学第一医院药剂科,甘肃兰州 730000;兰州大学第一医院国家药物临床试验机构办公室,甘肃兰州 730000;甘肃医学院附属医院,甘肃平凉 744000;陇西县第一人民医院,甘肃定西 748100;礼县第一人民医院,甘肃陇南 742200;临夏回族自治州人民医院,甘肃临夏 731100;金昌市中心医院,甘肃金昌 737100;兰州大学第一医院检验科,甘肃兰州 730000;兰州大学第一医院血液内科,甘肃兰州 730000 |
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基金项目: | 甘肃省新型冠状病毒肺炎(NCP)科技重大专项。 |
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摘 要: | 目的:建立一种快速、合理,且识别率高的新型冠状病毒感染肺炎的辅助诊断模型.方法:来自8个医疗机构的30例确诊病例的血清样本检测血常规指标,选取被排除COVID-19的其他患者和健康体检者的血清样本作为对照组,采用随机森林(random forest)方法建立识别模型,最终选取了8个重要指标,模型总准确率86.57%,对...
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关 键 词: | 新型冠状病毒肺炎 随机森林 血常规指标 机器学习 |
Establishment of a prediction model and auxiliary diagnosis for patients with COVID-19 |
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Affiliation: | (Department of Pharmacy,First Hospital of Lanzhou University,Gansu Lanzhou 730000) |
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Abstract: | Objective:The aim of this work is to establish a rapid,reasonable and high recognition model for COVID-19,which could be as an auxiliary diagnostic tool for clinicians.Results:Thirty confirmed cases from eight different medical institutions are included in this work,and 808 cases that are excluded patients with COVID-19 and healthy physical examination are considered as control group.Random forest(RF)algorithm was used to establish the recognition model based on blood routine indexes.ultimately the top-eight important indexes are selected.The accuracy of final model is 86.57%,and the positive samples(i.e.sensitivity)accuracy can achieve 91.67%.The internal and external validation methods are applied to verify the built model,respectively,and the results proved that the stability and reliability of the selected model.Conclusion:This work proposes a fast,economical,low labor cost and convenient COVID-19 pre-diagnosis tool,which is helpful for clinicians to provide valuable diagnostic information. |
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Keywords: | COVID-19 Random forest Routine blood indexes Machine learning |
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