Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
Ann Med. 2023 Dec;55(1):2233541. doi: 10.1080/07853890.2023.2233541.
The persistent spread of SARS-CoV-2 makes diagnosis challenging because COVID-19 symptoms are hard to differentiate from those of other respiratory illnesses. The reverse transcription-polymerase chain reaction test is the current golden standard for diagnosing various respiratory diseases, including COVID-19. However, this standard diagnostic method is prone to erroneous and false negative results (10% -15%). Therefore, finding an alternative technique to validate the RT-PCR test is paramount. Artificial intelligence (AI) and machine learning (ML) applications are extensively used in medical research. Hence, this study focused on developing a decision support system using AI to diagnose mild-moderate COVID-19 from other similar diseases using demographic and clinical markers. Severe COVID-19 cases were not considered in this study since fatality rates have dropped considerably after introducing COVID-19 vaccines.
A custom stacked ensemble model consisting of various heterogeneous algorithms has been utilized for prediction. Four deep learning algorithms have also been tested and compared, such as one-dimensional convolutional neural networks, long short-term memory networks, deep neural networks and Residual Multi-Layer Perceptron. Five explainers, namely, Shapley Additive Values, Eli5, QLattice, Anchor and Local Interpretable Model-agnostic Explanations, have been utilized to interpret the predictions made by the classifiers.
After using Pearson's correlation and particle swarm optimization feature selection, the final stack obtained a maximum accuracy of 89%. The most important markers which were useful in COVID-19 diagnosis are Eosinophil, Albumin, T. Bilirubin, ALP, ALT, AST, HbA1c and TWBC.
The promising results suggest using this decision support system to diagnose COVID-19 from other similar respiratory illnesses.
SARS-CoV-2 的持续传播使得诊断具有挑战性，因为 COVID-19 症状难以与其他呼吸道疾病区分开来。逆转录-聚合酶链反应 (RT-PCR) 测试是目前诊断各种呼吸道疾病（包括 COVID-19）的金标准。然而，这种标准诊断方法容易出现错误和假阴性结果（10%-15%）。因此，寻找一种替代技术来验证 RT-PCR 测试至关重要。人工智能 (AI) 和机器学习 (ML) 应用在医学研究中得到了广泛应用。因此，本研究专注于使用 AI 开发一种决策支持系统，使用人口统计学和临床标志物来诊断轻度至中度 COVID-19 与其他类似疾病。本研究不考虑严重 COVID-19 病例，因为 COVID-19 疫苗推出后死亡率已大幅下降。
使用包含各种异构算法的定制堆叠集成模型进行预测。还测试和比较了四种深度学习算法，例如一维卷积神经网络、长短期记忆网络、深度神经网络和残差多层感知机。使用了五种解释器，即 Shapley Additive Values、Eli5、QLattice、Anchor 和 Local Interpretable Model-agnostic Explanations，来解释分类器的预测结果。
使用 Pearson 相关系数和粒子群优化特征选择后，最终堆栈获得了 89%的最大准确性。对 COVID-19 诊断有用的最重要标志物是嗜酸性粒细胞、白蛋白、总胆红素、碱性磷酸酶、丙氨酸氨基转移酶、天冬氨酸氨基转移酶、糖化血红蛋白和白细胞总数。
有前途的结果表明，可以使用此决策支持系统从其他类似的呼吸道疾病中诊断 COVID-19。
