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Machine Learning Models for Predicting Antipsychotic Effectiveness and Separate Cost-Effectiveness Analysis in
Jiatong Zhang1, Qian Xu2, WenLong Jiang3
1College of Pharmacy, Qiqihar Medical University, Qiqihar, Heilongjiang, People's Republic of China.
Neuropsychiatric Disease and Treatment
|March 16, 2026
Summary
A machine learning model accurately predicts antipsychotic treatment efficacy in schizophrenia patients. Risperidone emerged as the most cost-effective treatment, optimizing patient care and reducing healthcare expenses.
Area of Science:
- Psychiatry and Mental Health
- Medical Informatics
- Health Economics
Background:
- Schizophrenia necessitates long-term antipsychotic treatment with variable patient responses.
- Treatment efficacy prediction is crucial for personalized medicine and cost management in schizophrenia care.
Purpose of the Study:
- To develop a machine learning (ML) model for predicting antipsychotic treatment efficacy in Chinese schizophrenia patients.
- To conduct a cost-effectiveness analysis from a payer's perspective to guide clinical and financial decisions.
Main Methods:
- A retrospective cohort study of 834 schizophrenia patients from a Chinese hospital.
- Construction and evaluation of eight ML models, with the Gradient Boosting Machine (GBM) model selected for its superior performance (AUC 0.879).
- Cost-effectiveness and sensitivity analyses were performed to identify the most economical treatment strategies.
Main Results:
- The developed GBM model achieved high accuracy (0.836) and recall (0.823) in predicting treatment efficacy.
- An online prediction calculator was created to support clinical decision-making.
- Risperidone was identified as the most cost-effective treatment regimen among those analyzed.
Conclusions:
- The GBM model and its online calculator can personalize schizophrenia treatment strategies for hospitalized patients.
- Risperidone demonstrates superior cost-effectiveness, aiding in treatment plan optimization and cost reduction.
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