Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Sci Rep. 2021 Feb 25;11(1):4673. doi: 10.1038/s41598-021-83967-7.
Predictors of the need for intensive care and mechanical ventilation can help healthcare systems in planning for surge capacity for COVID-19. We used socio-demographic data, clinical data, and blood panel profile data at the time of initial presentation to develop machine learning algorithms for predicting the need for intensive care and mechanical ventilation. Among the algorithms considered, the Random Forest classifier performed the best with [Formula: see text] for predicting ICU need and [Formula: see text] for predicting the need for mechanical ventilation. We also determined the most influential features in making this prediction, and concluded that all three categories of data are important. We determined the relative importance of blood panel profile data and noted that the AUC dropped by 0.12 units when this data was not included, thus indicating that it provided valuable information in predicting disease severity. Finally, we generated RF predictors with a reduced set of five features that retained the performance of the predictors trained on all features. These predictors, which rely only on quantitative data, are less prone to errors and subjectivity.
预测需要重症监护和机械通气的因素可以帮助医疗保健系统规划 COVID-19 的应对能力。我们使用初始表现时的社会人口统计学数据、临床数据和血液面板特征数据来开发用于预测重症监护和机械通气需求的机器学习算法。在所考虑的算法中，随机森林分类器在预测 ICU 需求方面表现最佳，[Formula: see text]，在预测机械通气需求方面表现最佳，[Formula: see text]。我们还确定了在进行此预测时最具影响力的特征，并得出结论，这三类数据都很重要。我们确定了血液面板特征数据的相对重要性，并注意到当不包括此数据时，AUC 下降了 0.12 个单位，这表明它在预测疾病严重程度方面提供了有价值的信息。最后，我们生成了具有减少的五个特征集的 RF 预测器，这些特征集保留了在所有特征上训练的预测器的性能。这些预测器仅依赖于定量数据，因此不太容易出错和主观。
