A Machine Learning Framework Using Real-World Data to Unveil Heterogeneity in Schizophrenia Patients

Music City Center 

Identifying patient subgroups with distinct clinical profiles can help personalize treatment, address unmet needs, and improve outcomes. To uncover these latent subgroups, we developed a 4-step machine-learning (ML) analytical framework to real-world claims data, including: (1) Automated feature extraction; (2) K-prototype clustering for subgroup identification; (3) XGBoost for risk factor selection; and (4) Advanced visualizations for clinical interpretability. We identified 3 schizophrenia patient subtypes initiating oral olanzapine, each with distinct characteristics, adherence patterns, and treatment outcomes. A high-risk subgroup with poor adherence had severe psychiatric comorbidities, heavier healthcare resource burden, more substance uses yet showed the strongest treatment effectiveness, suggesting a treatment option facilitating better adherence could improve outcomes. In contrast, the older multimorbid patient subgroup with better adherence had limited effectiveness. This study highlights the power of ML-driven analytical framework in uncovering patient heterogeneity using real-world data, providing a guidance for optimizing schizophrenia treatment in clinical practice.

machine learning

unsupervised clustering

feature engineering

real-world data

schizophrenia

personalized treatment 

Mental Health Statistics Section