Predicting antipsychotic responsiveness using a machine learning classifier trained on plasma levels of inflammatory markers in schizophrenia.

Abstract

We apply machine learning techniques to navigate the multifaceted landscape of schizophrenia. Our method entails the development of predictive models, emphasizing peripheral inflammatory biomarkers, which are classified into treatment response subgroups: antipsychotic-responsive, clozapine-responsive, and clozapine-resistant. The cohort comprises 146 schizophrenia patients (49 antipsychotics-responsive, 68 clozapine-responsive, 29 clozapine-resistant) and 49 healthy controls. Protein levels of immune biomarkers were quantified using the Olink Target 96 Inflammation Panel (Olink®, Uppsala, Sweden). To predict labels, a support vector machine (SVM) classifier is trained on the Olink®data matrix and evaluated via leave-one-out cross-validation. Associated protein biomarkers are identified via recursive feature elimination. We constructed three separate predictive models for binary classification: one to discern healthy controls from individuals with schizophrenia (AUC = 0.74), another to differentiate individuals who were responsive to antipsychotics (AUC = 0.88), and a third to distinguish treatment-resistant individuals (AUC = 0.78). Employing machine learning techniques, we identified features capable of distinguishing between treatment response subgroups. In this study, SVM demonstrates the power of machine learning to uncover subtle signals often overlooked by traditional statistics. Unlike t-tests, it handles multiple features simultaneously, capturing complex data relationships. Chosen for simplicity, robustness, and reliance on strong feature sets, its integration with explainable AI techniques like SHapely Additive exPlanations enhances model interpretability, especially for biomarker screening. This study highlights the potential of integrating machine learning techniques in clinical practice. Not only does it deepen our understanding of schizophrenia's heterogeneity, but it also holds promise for enhancing predictive accuracy, thereby facilitating more targeted and effective interventions in the treatment of this complex mental health disorder.