Target-based fusion using social determinants of health to enhance suicide prediction with electronic health records

• Published in: PloS one 2023-04, Vol.18 (4), p.e0283595-e0283595
• Main Authors: Sacco, Shane J, Chen, Kun, Wang, Fei, Aseltine, Robert
• Format: Article
• Language: English
• Subjects: Adolescent; Adolescents; Adult; Algorithms; Analysis; Child; Children; Codes; Computational linguistics; Computer and Information Sciences; Data collection; Data integration; Datasets; Diagnostic systems; Earth Sciences; Electronic Health Records; Electronic medical records; Electronic records; Health aspects; Health risks; Hospital patients; Hospitals; Humans; Juvenile offenders; Language processing; Longitudinal Studies; Machine learning; Mathematical models; Medical records; Medical research; Medicine and Health Sciences; Medicine, Experimental; Mental disorders; Mental health; Modelling; Modulus of elasticity; Natural language interfaces; Patients; Pediatrics; Physical Sciences; Psychological aspects; Research and Analysis Methods; Review boards; Risk Factors; Self destructive behavior; Sensitivity; Social aspects; Social Determinants of Health; Social factors; Social interactions; Social Sciences; Social support; Statistical analysis; Statistical models; Suicidal behavior; Suicide; Suicide, Attempted; Suicides & suicide attempts; Surveys; Surveys and Questionnaires; Teenagers; Young Adult; Young adults; Youth
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0283595

Preventing suicide in US youth is of paramount concern, with rates increasing over 50% between 2007 and 2018. Statistical modeling using electronic health records may help identify at-risk youth before a suicide attempt. While electronic health records contain diagnostic information, which are known risk factors, they generally lack or poorly document social determinants (e.g., social support), which are also known risk factors. If statistical models are built incorporating not only diagnostic records, but also social determinants measures, additional at-risk youth may be identified before a suicide attempt. Suicide attempts were predicted in hospitalized patients, ages 10-24, from the State of Connecticut's Hospital Inpatient Discharge Database (HIDD; N = 38943). Predictors included demographic information, diagnosis codes, and using a data fusion framework, social determinants features transferred or fused from an external source of survey data, The National Longitudinal Study of Adolescent to Adult Health (Add Health). Social determinant information for each HIDD patient was generated by averaging values from their most similar Add Health individuals (e.g., top 10), based upon matching shared features between datasets (e.g., Pearson's r). Attempts were then modelled using an elastic net logistic regression with both HIDD features and fused Add Health features. The model including fused social determinants outperformed the conventional model (AUC = 0.83 v. 0.82). Sensitivity and positive predictive values at 90 and 95% specificity were almost 10% higher when including fused features (e.g., sensitivity at 90% specificity = 0.48 v. 0.44). Among social determinants variables, the perception that their mother cares and being non-religious appeared particularly important to performance improvement. This proof-of-concept study showed that incorporating social determinants measures from an external survey database could improve prediction of youth suicide risk from clinical data using a data fusion framework. While social determinant data directly from patients might be ideal, estimating these characteristics via data fusion avoids the task of data collection, which is generally time-consuming, expensive, and suffers from non-compliance.