The strain on healthcare resources brought forth by the recent COVID-19 pandemic has highlighted the need for efficient resource planning and allocation through the prediction of future consumption. Machine learning can predict resource utilization such as the need for hospitalization based on past medical data stored in electronic medical records (EMR). We conducted this study on 3194 patients (46% male with mean age 56.7 (±16.8), 56% African American, 7% Hispanic) flagged as COVID-19 positive cases in 12 centers under Emory Healthcare network from February 2020 to September 2020, to assess whether a COVID-19 positive patient’s need for hospitalization can be predicted at the time of RT-PCR test using the EMR data prior to the test. Five main modalities of EMR, i.e., demographics, medication, past medical procedures, comorbidities, and laboratory results, were used as features for predictive modeling, both individually and fused together using late, middle, and early fusion. Models were evaluated in terms of precision, recall, F1-score (within 95% confidence interval). The early fusion model is the most effective predictor with 84% overall F1-score [CI 82.1–86.1]. The predictive performance of the model drops by 6 % when using recent clinical data while omitting the long-term medical history. Feature importance analysis indicates that history of cardiovascular disease, emergency room visits in the past year prior to testing, and demographic factors are predictive of the disease trajectory. We conclude that fusion modeling using medical history and current treatment data can forecast the need for hospitalization for patients infected with COVID-19 at the time of the RT-PCR test.

最近的 COVID-19 大流行给医疗资源带来的压力凸显了通过预测未来消费来进行有效资源规划和分配的必要性。机器学习可以根据电子病历 (EMR) 中存储的过去医疗数据预测资源利用率，例如住院需求。我们对 2020 年 2 月至 2020 年 9 月在埃默里医疗保健网络下的 12 个中心标记为 COVID-19 阳性病例的 3194 名患者（46% 男性，平均年龄 56.7 (±16.8)，56% 非裔美国人，7% 西班牙裔）进行了这项研究，评估是否可以在 RT-PCR 测试时使用测试前的 EMR 数据预测 COVID-19 阳性患者的住院需求。EMR 的五种主要模式，即人口统计学、药物治疗、既往医疗程序、合并症、和实验室结果被用作预测建模的特征，单独和使用晚期、中期和早期融合融合在一起。根据精度、召回率、F1 分数（在 95% 置信区间内）对模型进行评估。早期融合模型是最有效的预测因子，总 F1 分数为 84% [CI 82.1–86.1]。当使用最近的临床数据而忽略长期病史时，该模型的预测性能下降了 6%。特征重要性分析表明，心血管疾病史、测试前过去一年的急诊室就诊次数和人口统计学因素可预测疾病轨迹。