Browsing by keyword "Cluster analysis"
Now showing items 1-2 of 2
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Discovering prescription patterns in pediatric acute-onset neuropsychiatric syndrome patientsOBJECTIVE: Pediatric acute-onset neuropsychiatric syndrome (PANS) is a complex neuropsychiatric syndrome characterized by an abrupt onset of obsessive-compulsive symptoms and/or severe eating restrictions, along with at least two concomitant debilitating cognitive, behavioral, or neurological symptoms. A wide range of pharmacological interventions along with behavioral and environmental modifications, and psychotherapies have been adopted to treat symptoms and underlying etiologies. Our goal was to develop a data-driven approach to identify treatment patterns in this cohort. MATERIALS AND METHODS: In this cohort study, we extracted medical prescription histories from electronic health records. We developed a modified dynamic programming approach to perform global alignment of those medication histories. Our approach is unique since it considers time gaps in prescription patterns as part of the similarity strategy. RESULTS: This study included 43 consecutive new-onset pre-pubertal patients who had at least 3 clinic visits. Our algorithm identified six clusters with distinct medication usage history which may represent clinician's practice of treating PANS of different severities and etiologies i.e., two most severe groups requiring high dose intravenous steroids; two arthritic or inflammatory groups requiring prolonged nonsteroidal anti-inflammatory drug (NSAID); and two mild relapsing/remitting group treated with a short course of NSAID. The psychometric scores as outcomes in each cluster generally improved within the first two years. DISCUSSION AND CONCLUSION: Our algorithm shows potential to improve our knowledge of treatment patterns in the PANS cohort, while helping clinicians understand how patients respond to a combination of drugs.
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Profiles of Clinical Need Among Homeless Individuals with Dual DiagnosesThis study explored patterns of clinical need among homeless individuals with dual diagnoses, and explored whether certain profiles are characteristic of different demographic groups. Data were drawn from two larger studies conducted with dually diagnosed, homeless individuals (n = 373). Hierarchical cluster analysis identified four subgroups: (1) Clinically least severe, characterized by less frequent psychological symptoms and no history of physical or sexual abuse; (2) Moderate clinical needs, including shorter history of substance use and less frequent psychological symptoms, but symptoms consistent with severe mental illness; (3) Clinically severe, with frequent anxiety, depression, past and recent physical or sexual abuse, and long history of substance use; (4) Least frequent psychological symptoms, but frequent history of physical or sexual abuse and long history of drug use. Women veterans were mostly likely to be classified in cluster 3, and male civilians in cluster 2. Subgroups of homeless individuals with dual diagnoses demonstrated different clusters of clinical needs, having implications for service delivery to the population.
