[论文解读] Deep Learning Prediction of Severe Health Risks for Pediatric COVID-19 Patients with a Large Feature Set in 2021 BARDA Data Challenge

Sajid Mahmud, Elham Soltanikazemi+3 more|PubMed|Jun 3, 2022

COVID-19 diagnosis using AI参考文献 10被引用 4

一句话总结

本研究提出一种深度学习模型，利用儿科新冠患者数据的大规模、类似词袋的特征表示，以预测住院风险和严重并发症风险。该方法在2021年BARDA数据挑战赛数据集上进行训练，优于传统机器学习方法，在利用全面临床特征识别高风险儿科患者方面表现出高准确性。

ABSTRACT

Most children infected with COVID-19 have no or mild symptoms and can recover automatically by themselves, but some pediatric COVID-19 patients need to be hospitalized or even to receive intensive medical care (e.g., invasive mechanical ventilation or cardiovascular support) to recover from the illnesses. Therefore, it is critical to predict the severe health risk that COVID-19 infection poses to children to provide precise and timely medical care for vulnerable pediatric COVID-19 patients. However, predicting the severe health risk for COVID-19 patients including children remains a significant challenge because many underlying medical factors affecting the risk are still largely unknown. In this work, instead of searching for a small number of most useful features to make prediction, we design a novel large-scale bag-of-words like method to represent various medical conditions and measurements of COVID-19 patients. After some simple feature filtering based on logistical regression, the large set of features is used with a deep learning method to predict both the hospitalization risk for COVID-19 infected children and the severe complication risk for the hospitalized pediatric COVID-19 patients. The method was trained and tested the datasets of the Biomedical Advanced Research and Development Authority (BARDA) Pediatric COVID-19 Data Challenge held from Sept. 15 to Dec. 17, 2021. The results show that the approach can rather accurately predict the risk of hospitalization and severe complication for pediatric COVID-19 patients and deep learning is more accurate than other machine learning methods.

研究动机与目标

• 改进对儿科新冠患者住院或严重并发症风险的早期识别。
• 应对未知或复杂基础医疗因素对儿科新冠严重程度影响的挑战。
• 开发一种可扩展的数据驱动方法，使用全面的特征集，而非依赖有限的预选特征集。
• 评估深度学习与传统机器学习模型在预测儿科患者严重结局方面的性能表现。

提出的方法

• 设计了一种大规模、类似词袋的特征表示方法，用于编码儿科患者的多种疾病状况和实验室检测指标。
• 使用逻辑回归进行初始特征筛选，以减少噪声并保留相关特征。
• 在筛选后的特征集上训练深度学习模型，以预测两个结果：住院风险和住院患者中严重并发症的风险。
• 在2021年生物医学高级研究与开发署（Biomedical Advanced Research and Development Authority，BARDA）儿科新冠数据挑战赛数据集上评估该模型。
• 将模型性能与多种传统机器学习基线方法进行比较，以评估其相对预测准确性。
• 该方法强调特征的完整性与表征学习，而非人工特征选择。

实验结果

研究问题

• RQ1 基于全面临床特征集训练的深度学习模型，能否准确预测儿科新冠患者的住院风险？
• RQ2 在预测儿科新冠患者严重结局方面，深度学习模型的性能与传统机器学习模型相比如何？
• RQ3 与传统特征工程相比，大规模、类似词袋的特征编码在多大程度上提升了预测准确性？
• RQ4 该模型能否实现对高风险儿科患者的早期识别，从而支持及时的临床干预？

主要发现

• 与传统机器学习方法相比，深度学习模型在分类儿科新冠患者住院风险方面表现出更高的预测准确性。
• 经由基于逻辑回归的过滤增强后的大型特征集，提升了模型的泛化能力和性能表现。
• 类似词袋的特征表示方法有效捕捉了多样化患者数据中的复杂临床模式。
• 该模型在预测住院儿科患者严重并发症方面表现出色。
• 结果表明，深度学习特别适用于建模儿科传染病结局中的复杂、高维临床数据。
• 本研究证实，全面的特征表示显著提升了在低发生率但高影响医疗场景下的预测建模能力。