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Identifying Latent Patterns of Intimate Partner Violence Using Electronic Health Records and Association Rule Mining.

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Identifying intimate partner violence (IPV) in emergency departments is difficult. Association rule mining of electronic health records reveals distinct patterns in IPV survivors, aiding in improved detection and clinical decision-making.

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Area of Science:

  • Public Health
  • Health Informatics
  • Data Mining

Background:

  • Intimate partner violence (IPV) is a major public health issue.
  • Identifying IPV in emergency departments (EDs) is challenging due to inconsistent use of IPV-related ICD-10 codes.
  • Electronic health records (EHRs) offer a potential data source for IPV detection.

Purpose of the Study:

  • To employ association rule mining on EHR data to identify latent patterns indicative of IPV among female patients in EDs.
  • To differentiate patterns between confirmed IPV survivors and non-IPV patients.
  • To explore informatics-driven approaches for enhancing IPV detection in clinical settings.

Main Methods:

  • Applied the Apriori algorithm to EHR data.
  • Analyzed associations between co-occurring health conditions in confirmed IPV survivors and non-IPV patients.
  • Utilized data mining techniques to uncover latent patterns.

Main Results:

  • Distinct patterns were identified between IPV survivors and non-IPV patients.
  • IPV survivors showed stronger associations between physical injuries, mental health conditions (depression, suicidal ideation), and socioeconomic stressors.
  • Non-IPV cases were primarily linked to anxiety disorders and family-related stressors.

Conclusions:

  • Informatics-driven approaches using EHR data can improve IPV detection by revealing subtle clinical signals.
  • The study highlights the potential for data-driven tools to enhance IPV screening and clinical decision-making in EDs.
  • Understanding these patterns can lead to more effective interventions for IPV survivors.