Machine Learning Models for Predicting Antipsychotic Effectiveness and Separate Cost-Effectiveness Analysis in...

Schizophrenia is a burden on patients' health and finances and long-term antipsychotic treatment is required; treatment response differs among patients. This study aims to leverage data from Chinese hospitals to develop a machine learning (ML) model that predicts antipsychotic treatment efficacy in patients with schizophrenia and to conduct a payer-perspective cost-effectiveness analysis to inform clinical practice.

This single-center, real-world retrospective cohort study included 834 patients with schizophrenia from a Chinese hospital. Eight models were constructed using ML and performance was assessed. The model with highest accuracy was determined based on the area under the receiver operating characteristic curve (AUC). We used the Shapley Additive Explanations (SHAP) values to determine the relative importance of each factor. Cost-effectiveness and incremental cost-effectiveness analyses were performed to assess cost-effectiveness of various treatments. A univariate sensitivity analysis was also conducted to validate the results.

The top 10 strongly correlated variables, identified through the Boruta algorithm, were selected for in-depth analysis to construct the model. GBM demonstrates the highest performance following a comprehensive evaluation. On the independent test set, our model achieved an AUC of 0.879 (95% CI: 0.833-0.924), an accuracy of 0.836, and a recall of 0.823. Based on this model, we developed and made publicly available an online prediction calculator to assist in clinical decision-making. Among all the treatment regimens, risperidone was the most cost-effective.

The GBM model and its online calculator predict the treatment efficacy for hospitalized schizophrenia patients, aiding doctors in tailoring personalised treatment strategies. Risperidone tablets exhibit the highest cost-effectiveness in treatment, guiding the optimization of treatment plans and cost reduction.