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The discharge summary is crucial as it enables a smooth transition from a healthcare facility to a patient's home or another care setting. This critical document facilitates seamless continuity of care, ensuring patients receive the necessary support and attention.
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Predicting adolescent suicidal behavior following inpatient discharge using structured and unstructured data.

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|December 29, 2023
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Summary

A new algorithm using electronic health records can predict suicide-related ICD codes in youth within three months of psychiatric discharge. This tool aids in assessing suicide risk for better patient care.

Keywords:
AdolescenceElectronic health recordsMachine learningPatient dischargeRiskSuicide

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Area of Science:

  • * Clinical Informatics
  • * Machine Learning in Healthcare
  • * Adolescent Psychiatry

Background:

  • * Developing predictive algorithms for suicide risk is crucial for timely intervention.
  • * Utilizing electronic health record (EHR) data offers a rich source for identifying at-risk individuals.

Purpose of the Study:

  • * To create and evaluate an algorithm for predicting suicide-related International Classification of Diseases (ICD) codes.
  • * To assess the algorithm's performance in identifying youth at high risk for suicidal behavior post-psychiatric discharge.

Main Methods:

  • * Retrospective cohort study of 2789 adolescents (12-20 years) from a Northeastern US safety net institution.
  • * Combined structured EHR data with unstructured clinical notes processed via natural language processing.
  • * Compared machine learning models, including gradient boosting and random forest analyses.

Main Results:

  • * The Gradient Boosting model achieved an Area Under the ROC curve of 0.88.
  • * The model demonstrated 80% sensitivity and 76% specificity at an optimal cutoff probability of 0.009.
  • * The algorithm correctly identified 8 out of 10 positive cases in the testing set.

Conclusions:

  • * Integrating structured and unstructured EHR data improves predictive algorithms for suicidal behavior.
  • * The developed algorithm shows promise for clinical integration into psychiatric services to enhance suicide risk assessment.
  • * Further validation across multiple healthcare systems is recommended to mitigate potential bias.