The Role of Cluster Analysis in Precision Psychiatry: A Systematic Review of Subgroup Identification in Psychiatric Disorders

Mental health disorders are highly heterogeneous, presenting diverse symptoms, risk factors,and treatment responses. Identifying distinct subgroups within these disorders is essential fordeveloping personalized treatment strategies, improving therapeutic outcomes, andoptimizing resource allocation. This systematic review explores how cluster analysis has beenapplied in mental health research to categorize subgroups across various psychiatric andpsychological populations, evaluating its implications for personalized care and addressingkey methodological challenges. A comprehensive literature search of PubMed, PsycINFO,Scopus, and Web of Science (October 2021 to October 2024) yielded 31 studies that usedcluster analysis to identify subgroups within disorders such as depression, PTSD, anxiety,schizophrenia, BPD, ADHD, and OCD. In alignment with the study’s objectives, dataextraction focused on clustering methodologies, subgroup characteristics, and the clinicalimplications for treatment personalization. Studies employed a range of clustering techniques,including K-means, hierarchical clustering, latent class analysis, Gaussian mixture models,and DBSCAN, which effectively identified clinically meaningful subgroups characterized byunique symptom profiles, biological markers, and treatment responses. For instance,melancholic, atypical, and anxious subtypes in depression were identified, each requiringtailored therapeutic approaches. Similarly, biomarker-based subgroups in generalized anxietydisorder emphasized the potential for targeted interventions. This review affirms that clusteranalysis is a valuable tool in precision psychiatry, offering insights into disorderheterogeneity that support the development of individualized treatment plans and improvepatient outcomes.Keywords: Cluster analysis, mental health, subgroup identification, personalized care,systematic review, precision psychiatry