Development of a machine learning prognostic model for early prediction of scrub typhus progression at hospital admission based on clinical and laboratory features.

BACKGROUND

Scrub typhus (ST) is a life-threatening infectious disease caused by . Early prediction of whether the disease will progress to a severe state is crucial for clinicians to provide targeted medical care in advance.

METHODS

This study retrospectively collected severe and mild ST cases in two hospitals in Fujian Province, China from 2011 to 2022. Eighteen objective clinical and laboratory features collected at admission were screened using various feature selection algorithms, and used to construct models based on six machine learning algorithms.

RESULTS

The model based on Gradient Boosting Decision Tree using 14 features screened by Recursive Feature Elimination was evaluated as the optimal one. The model showed high accuracy, precision, sensitivity, specificity, F-1 score, and area under receiver operating characteristics curve of 0.975, 0.967, 0.983, 0.966, 0.975, and 0.981, respectively, indicating its possible clinical application. Additionally, a simplified model based on Support Vector Machine was constructed and evaluated as an alternative optimal model.

CONCLUSIONS

This study is the first to use machine learning algorithms to accurately predict the developments of ST patients upon admission to hospitals. The models can help clinicians assess the potential risks of their patients early on, thereby improving patient outcomes.