Combined with the semantic features of CT and selected clinical variables, a machine learning model for accurately predicting the prognosis of Omicron was established.

Objectives: To efficiently use medical resources and offer optimal personalized treatment for individuals with Omicron infection, it is vital to predict the disease's outcome early on. This research developed three machine learning models to foresee the results for Omicron-infected patients. Methods: Data from 253 Omicron-infected patients, including their CT scans, clinical details, and relevant laboratory values, were studied. The patients were categorized into two groups based on their disease progression: favourable prognosis and unfavourable prognosis. Patients manifesting respiratory failure, acute liver or kidney impairment, or fatalities were placed in the "poor" group. Those lacking such symptoms were allocated to the "good" group. The participants were randomly split into training set (202) and validation set (51) with an 8:2 ratio. Radiomics features were produced using image processing, focused segmentation, feature extraction, and selection, leading to the establishment of a radiomics model. A univariate logistic regression method identified potential clinical factors contributing to a clinical model's development. Eventually, the fused feature set, integrating radiomics features and clinical indicators, was used for the combined model. The model's prediction performance was assessed using the area under the receiver operating characteristic curve (AUC). The model's clinical usefulness was evaluated by generating calibration and decision curves. Results: Compared to other classification models, the combined model showcased the best classification performance. It achieved an AUC of 0.848 and accuracy of 0.763 in the training set, and 0.797 and 0.750 in the validation set, respectively. Conclusions: This study employed machine learning model to accurately predict the prognosis of Omicron-infected patients. Advances in knowledge: (1) Topic innovation: At present, there is a lack of research on the use of CT images to construct machine learning models to predict the prognosis of patients with Omicron infection. This study intends to establish clinical, radiomics, and combined models to provide more possibilities for the identification of the two. (2) Platform innovation: The feature extraction and screening and the establishment of omics model in this study will be completed in the intelligent scientific research platform, which can reduce the error caused by human error, simplify the operation steps, and save the time of data processing time.