Introduction: Severe fever with thrombocytopenia syndrome virus (SFTSV) is an emerging tick-borne virus associated with a high rate of mortality, as well as encephalitis. We aim to develop and validate a machine learning model to early predict the potential life-threatening conditions of SFTS.
Methods: The clinical presentation, demographic information, and laboratory parameters from 327 patients with SFTS at admission in three large tertiary hospitals in Jiangsu, China between 2010 to 2022 are retrieved. We establish a reservoir computing with boosted topology (RC-BT) algorithm to obtain the models' predictions of the encephalitis and mortality of patients with SFTS. The prediction performances of encephalitis and mortality are further tested and validated. Finally, we compare our RC-BT model with the other traditional machine learning algorithms including Lightgbm, support vector machine (SVM), Xgboost, Decision Tree, and Neural Network (NN).
Results: For the prediction of encephalitis among patients with SFTS, nine parameters are selected with equal weight, namely calcium, cholesterol, muscle soreness, dry cough, smoking history, temperature at admission, troponin T, potassium, and thermal peak. The accuracy for the validation cohort by the RC-BT model is 0.897 [95% confidence interval (CI) 0.873-0.921]. The sensitivity and negative predictive value (NPV) of the RC-BT model are 0.855 (95% CI 0.824-0.886) and 0.904 (95% CI 0.863-0.945), respectively. Area under curve of the RC-BT model for the validation cohort is 0.899 (95% CI 0.882-0.916). For the prediction of fatality among patients with SFTS, seven parameters are selected with equal weight, namely calcium, cholesterol, history of drinking, headache, field contact, potassium, and dyspnea. The accuracy of the RC-BT model is 0.903 (95% CI 0.881-0.925). The sensitivity and NPV of the RC-BT model are 0.913 (95% CI 0.902-0.924) and 0.946 (95% CI 0.917-0.975), respectively. The area under curve is 0.917 (95% CI 0.902-0.932). Importantly, the RC-BT models outperform the other artificial intelligence-based algorithms in both prediction tasks.
Conclusions: Our two RC-BT models of SFTS encephalitis and fatality demonstrate high area under curves, specificity, and NPV, with nine and seven routine clinical parameters, respectively. Our models can not only greatly improve the early prognosis accuracy of SFTS, but can also be widely applied in underdeveloped areas with limited medical resources.
Keywords: Encephalitis; Fatality; Machine learning; SFTS.
© 2023. The Author(s).