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Identification of suicidal behavior among psychiatrically hospitalized adolescents using natural language processing and machine learning of electronic health records

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  • Nicholas J Carson
  • Brian Mullin
  • Maria Jose Sanchez
  • Frederick Lu
  • Kelly Yang
  • Michelle Menezes
  • Benjamin Lê Cook

Abstract

Objective: The rapid proliferation of machine learning research using electronic health records to classify healthcare outcomes offers an opportunity to address the pressing public health problem of adolescent suicidal behavior. We describe the development and evaluation of a machine learning algorithm using natural language processing of electronic health records to identify suicidal behavior among psychiatrically hospitalized adolescents. Methods: Adolescents hospitalized on a psychiatric inpatient unit in a community health system in the northeastern United States were surveyed for history of suicide attempt in the past 12 months. A total of 73 respondents had electronic health records available prior to the index psychiatric admission. Unstructured clinical notes were downloaded from the year preceding the index inpatient admission. Natural language processing identified phrases from the notes associated with the suicide attempt outcome. We enriched this group of phrases with a clinically focused list of terms representing known risk and protective factors for suicide attempt in adolescents. We then applied the random forest machine learning algorithm to develop a classification model. The model performance was evaluated using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. Results: The final model had a sensitivity of 0.83, specificity of 0.22, AUC of 0.68, a PPV of 0.42, NPV of 0.67, and an accuracy of 0.47. The terms mostly highly associated with suicide attempt clustered around terms related to suicide, family members, psychiatric disorders, and psychotropic medications. Conclusion: This analysis demonstrates modest success of a natural language processing and machine learning approach to identifying suicide attempt among a small sample of hospitalized adolescents in a psychiatric setting.

Suggested Citation

  • Nicholas J Carson & Brian Mullin & Maria Jose Sanchez & Frederick Lu & Kelly Yang & Michelle Menezes & Benjamin Lê Cook, 2019. "Identification of suicidal behavior among psychiatrically hospitalized adolescents using natural language processing and machine learning of electronic health records," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-14, February.
  • Handle: RePEc:plo:pone00:0211116
    DOI: 10.1371/journal.pone.0211116
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    References listed on IDEAS

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    1. Alexander J Millner & Michael D Lee & Matthew K Nock, 2015. "Single-Item Measurement of Suicidal Behaviors: Validity and Consequences of Misclassification," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-17, October.
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    Cited by:

    1. Abayomi Arowosegbe & Tope Oyelade, 2023. "Application of Natural Language Processing (NLP) in Detecting and Preventing Suicide Ideation: A Systematic Review," IJERPH, MDPI, vol. 20(2), pages 1-23, January.
    2. Sri Banerjee & Pat Dunn & Scott Conard & Asif Ali, 2024. "Mental Health Applications of Generative AI and Large Language Modeling in the United States," IJERPH, MDPI, vol. 21(7), pages 1-12, July.
    3. Rebecca A. Bernert & Amanda M. Hilberg & Ruth Melia & Jane Paik Kim & Nigam H. Shah & Freddy Abnousi, 2020. "Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations," IJERPH, MDPI, vol. 17(16), pages 1-25, August.

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