Prediction of Disease Progression of COVID-19 Based upon Machine Learning

Abstract

Background Since December 2019, COVID-19 has spread throughout the world. Clinical outcomes of COVID-19 patients vary among infected individuals. Therefore, it is vital to identify patients at high risk of disease progression. Methods In this retrospective, multicenter cohort study, COVID-19 patients from Huoshenshan Hospital and Taikang Tongji Hospital (Wuhan, China) were included. Clinical features showing significant differences between the severe and nonsevere groups were screened out by univariate analysis. Then, these features were used to generate classifier models to predict whether a COVID-19 case would be severe or nonsevere based on machine learning. Two test sets of data from the two hospitals were gathered to evaluate the predictive performance of the models. Results A total of 455 patients were included, and 21 features showing significant differences between the severe and nonsevere groups were selected for the training and validation set. The optimal subset, with eleven features in the k –nearest neighbor model, obtained the highest area under the curve (AUC) value among the four models in the validation set. D-dimer, CRP, and age were the three most important features in the optimal-feature subsets. The highest AUC value was obtained using a support vector–machine model for a test set from Huoshenshan Hospital. Software for predicting disease progression based on machine learning was developed. Conclusion The predictive models were successfully established based on machine learning, and achieved satisfactory predictive performance of disease progression with optimal-feature subsets.

Recent advancement in integrating artificial intelligence and information technology with real‐world data for clinical decision‐making in China: A scoping review

“…45 For clinical EMRs, single-center EMRs were adopted by seven studies, 37,41,42,47,[50][51][52] and another seven studies used multicenter EMRs. [38][39][40]46,48,49,53 One additional study leveraged disease registry, the China Acute Myocardial Infarction registry. 43 Purposes of developed tools varied from predicting populations at risks, including women who are at high risk of gestational diabetes in early pregnancy 45 and patients who are at risk of progression to severe COVID-19, 48,49 to predicting both short-term and long-term disease risk and prognosis.…”

“…The extreme gradient boosting (XGBoost) algorithm was found to be a popular ML technique. [38][39][40]44,45,47 Other algorithms included gradient boosted tree, 36 artificial neural network, 42 random forest, 37,53 k-nearest neighbor, 48 logistic regression, 51 and Naïve Bayes. 52 Evidential reasoning rule and focused-CNN with Boruta algorithm were used respectively to select features of ICU admission and in-hospital mortality of trauma patients 50 and patients who were at risk of progression to severe COVID-19.…”

“…Specifically, eight studies (44.4%) constructed tools for cardiovascular and cerebrovascular diseases, [36][37][38][39][40][41][42][43] two for diabetes, 44,45 one for IgA nephropathy, 46 and one for hypertension. 47 Other disease areas included coronavirus disease 2019 (COVID-19), 48,49 trauma and injury outcomes, 50,51 surgical site infection, 52 and myopia. 53…”

“…[38][39][40]46,48,49,53 One additional study leveraged disease registry, the China Acute Myocardial Infarction registry. 43 Purposes of developed tools varied from predicting populations at risks, including women who are at high risk of gestational diabetes in early pregnancy 45 and patients who are at risk of progression to severe COVID-19, 48,49 to predicting both short-term and long-term disease risk and prognosis. Among predictive and prognostic tools, seven studies predicted long-term risk and prognosis [36][37][38][39]44,46,53 and eight tools explored short-term risk and prognosis.…”

“…The retrospective use of RWD was the predominate method of data collection. [36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53] Similar to diagnostic tools, sample sizes varied by the types of RWD sources. For tools derived from population-based cohorts and registry, the sample sizes ranged from 15k 44 to 503k.…”