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Identifying and characterizing suicide decedent subtypes using deep embedded clustering.

Anas Belouali1, Christopher Kitchen2, Ayah Zirikly3

  • 1Division of General Internal Medicine, Biomedical Informatics and Data Science (BIDS), Johns Hopkins School of Medicine, 2024 East Monument St. S 1-200, Baltimore, MD, 21205, USA. abeloua1@jhu.edu.

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Summary
This summary is machine-generated.

Researchers identified four distinct suicide profiles using linked health and public health data. These profiles highlight demographic and clinical differences, informing targeted suicide prevention strategies.

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Area of Science:

  • Public Health
  • Data Science
  • Psychiatry

Background:

  • Population-level studies of suicide decedent subtypes using linked clinical and public health data are lacking.
  • Identifying distinct suicide profiles is crucial for developing effective, targeted prevention strategies.

Purpose of the Study:

  • To identify and characterize distinct subtypes of suicide decedents using linked clinical and public health data.
  • To evaluate the utility of deep embedded clustering for suicide profile identification.

Main Methods:

  • Retrospective analysis of the Maryland Suicide Data Warehouse (MSDW) from 2016-2019.
  • Inclusion of 848 suicide decedents and 4,161 accidental death decedents with linked electronic health records and hospital discharge data.
  • Application of deep embedded clustering (k=2-10) with stability metrics (cross-validated prediction strength of 0.94) for profile characterization.

Main Results:

  • Four distinct suicide profiles were identified with significant demographic and clinical variations.
  • Profile 1 (23.2%): Older individuals with high comorbidity.
  • Profile 2 (19.2%): Psychiatric illness, high healthcare utilization, significant social needs.
  • Profile 3 (25.4%): Younger individuals, psychiatric illness, no social needs, high Medicaid use.
  • Profile 4 (32.2%): Less clinically engaged, fewer healthcare visits.

Conclusions:

  • Deep embedded clustering effectively identifies meaningful and stable suicide decedent profiles.
  • The identified profiles reveal significant differences in demographics, clinical factors, and healthcare engagement.
  • These distinct profiles can inform the development and deployment of tailored, population-level suicide prevention strategies.