A multi-instance support vector machine with incomplete data for clinical outcome prediction of COVID-19
A multi-instance support vector machine with incomplete data for clinical outcome prediction of COVID-19
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DOI：
10.1145/3459930.3469552
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Lodewijk Brand;L. Baker;Hua Wang
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文献类型：
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作者：
Lodewijk Brand;L. Baker;Hua Wang
为了管理与COVID-19相关的公共卫生危机，医护人员能够快速识别高风险患者以在有限资源下提供有效治疗至关重要。统计学习工具有可能帮助预测疾病进展早期的严重感染。然而，这些技术中的许多技术无法充分利用每个患者的时间数据，因为它们将问题处理为单实例分类。此外，这些算法依赖于完整的数据来进行预测。在这项工作中，我们提出了一种新的方法来同时处理时间和缺失数据问题;我们提出的同步插补-多实例支持向量机方法说明了如何利用多实例学习技术和低秩数据插补来准确预测COVID-19患者的临床结果。我们将我们的方法与最近用于预测361名COVID-19阳性患者的公共数据集结果的方法进行了比较。除了在疾病进展的早期提高预测性能外，我们的方法还确定了一系列与肝脏，免疫系统和血液相关的生物标志物，这些生物标志物值得进一步研究，并可能为COVID-19导致的患者死亡原因提供额外的见解。我们在线发布了我们方法的源代码。1
In order to manage the public health crisis associated with COVID-19, it is critically important that healthcare workers can quickly identify high-risk patients in order to provide effective treatment with limited resources. Statistical learning tools have the potential to help predict serious infection early-on in the progression of the disease. However, many of these techniques are unable to take full advantage of temporal data on a per-patient basis as they handle the problem as a single-instance classification. Furthermore, these algorithms rely on complete data to make their predictions. In this work, we present a novel approach to handle the temporal and missing data problems, simultaneously; our proposed Simultaneous Imputation-Multi Instance Support Vector Machine method illustrates how multiple instance learning techniques and low-rank data imputation can be utilized to accurately predict clinical outcomes of COVID-19 patients. We compare our approach against recent methods used to predict outcomes on a public dataset with a cohort of 361 COVID-19 positive patients. In addition to improved prediction performance early on in the progression of the disease, our method identifies a collection of biomarkers associated with the liver, immune system, and blood, that deserve additional study and may provide additional insight into causes of patient mortality due to COVID-19. We publish the source code for our method online.1