Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach.
Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach.
复制标题
准确区分 COVID-19 患者、其他病毒感染患者和健康个体：多模式后期融合学习方法。
DOI：
10.2196/25535
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发表时间：
2021-01-06
影响因子：
7.4
通讯作者：
Chen S
中科院分区：
文献类型：
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作者：
Xu M;Ouyang L;Han L;Sun K;Yu T;Li Q;Tian H;Safarnejad L;Zhang H;Gao Y;Bao FS;Chen Y;Robinson P;Ge Y;Zhu B;Liu J;Chen S
利用非聚合酶链反应生物医学数据有效识别COVID-19患者对于实现最佳临床结果至关重要。目前，缺乏对各种生物医学特征的全面认识和适当的分析方法，无法早期发现和有效诊断COVID-19患者。我们的目的是结合低维临床和实验室检测数据以及高维计算机断层扫描（CT）成像数据，准确区分健康个体、COVID-19患者和非COVID-19病毒性肺炎患者，特别是在感染的早期。在本研究中，我们招募了214例非重症COVID-19患者、148例重症COVID-19患者、198例未感染的健康参与者和129例非COVID-19病毒性肺炎患者。获取参与者的临床信息（即23个特征）、实验室测试结果（即10个特征）和入院时的CT扫描，并将其用作3种输入特征模态。为了实现多模态特征的后期融合，我们构建了一个深度学习模型来提取CT扫描的10个特征的高级表示。然后，我们基于所有3种模式的43个特征开发了3个机器学习模型（即k近邻、随机森林和支持向量机模型），以区分以下4类：非严重、严重、健康和病毒性肺炎。多模态特性从使用任何单一特征模态中获得了可观的性能增益。3种机器学习模型均具有较高的整体预测准确率（95.4% ~ 97.7%）和较高的分类预测准确率（90.6% ~ 99.9%）。相比于现有的二元分类基准往往集中在单一特征模态上，本研究的混合深度学习-机器学习框架为临床应用提供了新的有效突破。我们的研究结果来自相对较大的样本量，分析工作流程将补充和协助当前COVID-19诊断方法和其他具有高维多模态生物医学特征的临床应用的临床决策支持。
Effectively identifying patients with COVID-19 using nonpolymerase chain reaction biomedical data is critical for achieving optimal clinical outcomes. Currently, there is a lack of comprehensive understanding in various biomedical features and appropriate analytical approaches for enabling the early detection and effective diagnosis of patients with COVID-19. We aimed to combine low-dimensional clinical and lab testing data, as well as high-dimensional computed tomography (CT) imaging data, to accurately differentiate between healthy individuals, patients with COVID-19, and patients with non-COVID viral pneumonia, especially at the early stage of infection. In this study, we recruited 214 patients with nonsevere COVID-19, 148 patients with severe COVID-19, 198 noninfected healthy participants, and 129 patients with non-COVID viral pneumonia. The participants’ clinical information (ie, 23 features), lab testing results (ie, 10 features), and CT scans upon admission were acquired and used as 3 input feature modalities. To enable the late fusion of multimodal features, we constructed a deep learning model to extract a 10-feature high-level representation of CT scans. We then developed 3 machine learning models (ie, k-nearest neighbor, random forest, and support vector machine models) based on the combined 43 features from all 3 modalities to differentiate between the following 4 classes: nonsevere, severe, healthy, and viral pneumonia. Multimodal features provided substantial performance gain from the use of any single feature modality. All 3 machine learning models had high overall prediction accuracy (95.4%-97.7%) and high class-specific prediction accuracy (90.6%-99.9%). Compared to the existing binary classification benchmarks that are often focused on single-feature modality, this study’s hybrid deep learning-machine learning framework provided a novel and effective breakthrough for clinical applications. Our findings, which come from a relatively large sample size, and analytical workflow will supplement and assist with clinical decision support for current COVID-19 diagnostic methods and other clinical applications with high-dimensional multimodal biomedical features.