A Review on Diagnosis and Management of Treatment-Resistant Schizophrenia Based on Clinical Big Data

Abstract

Treatment-resistant schizophrenia (TRS), a condition affecting approximately 30% of individuals diagnosed with schizophrenia, persists as a major therapeutic challenge in contemporary psychiatry. Despite advancements in antipsychotic pharmacotherapy, a substantial subset of patients demonstrates suboptimal responses to conventional interventions. This comprehensive review synthesizes evidence derived from clinical big data analyses to elucidate epidemiological trends, pathophysiological mechanisms, diagnostic advancements, and innovative treatment modalities for TRS. By integrating multi-source data from electronic health records (EHRs), genomic repositories, neuroimaging databases, and real-world outcome studies, this article delineates data-driven strategies to enhance personalized treatment protocols and prognostic outcomes. Emerging methodologies, including artificial intelligence (AI)-enhanced predictive analytics and multi-omics integration, are critically evaluated to delineate future research trajectories in TRS management.