Ability of clinical data to predict readmission in Child and Adolescent Mental Health Services.

ABSTRACT: This study addresses the challenge of predicting readmissions in Child and Adolescent Mental Health Services (CAMHS) by analyzing the predictability of readmissions over short, medium, and long term periods. Using health records spanning 35 years, which included 22,643 patients and 30,938 episodes of care, we focused on the episode of care as a central unit, defined as a referral-discharge cycle that incorporates assessments and interventions. Data pre-processing involved handling missing values, normalizing, and transforming data, while resolving issues related to overlapping episodes and correcting registration errors where possible. Readmission prediction was inferred from electronic health records (EHR), as this variable was not directly recorded. A binary classifier distinguished betw

SUBMITTER: Koochakpour K 

PROVIDER: S-EPMC11622991 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature