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Predicting the Risk of Suicide by Analyzing the Text of Clinical Notes

Author

Listed:
  • Chris Poulin
  • Brian Shiner
  • Paul Thompson
  • Linas Vepstas
  • Yinong Young-Xu
  • Benjamin Goertzel
  • Bradley Watts
  • Laura Flashman
  • Thomas McAllister

Abstract

We developed linguistics-driven prediction models to estimate the risk of suicide. These models were generated from unstructured clinical notes taken from a national sample of U.S. Veterans Administration (VA) medical records. We created three matched cohorts: veterans who committed suicide, veterans who used mental health services and did not commit suicide, and veterans who did not use mental health services and did not commit suicide during the observation period (n = 70 in each group). From the clinical notes, we generated datasets of single keywords and multi-word phrases, and constructed prediction models using a machine-learning algorithm based on a genetic programming framework. The resulting inference accuracy was consistently 65% or more. Our data therefore suggests that computerized text analytics can be applied to unstructured medical records to estimate the risk of suicide. The resulting system could allow clinicians to potentially screen seemingly healthy patients at the primary care level, and to continuously evaluate the suicide risk among psychiatric patients.

Suggested Citation

  • Chris Poulin & Brian Shiner & Paul Thompson & Linas Vepstas & Yinong Young-Xu & Benjamin Goertzel & Bradley Watts & Laura Flashman & Thomas McAllister, 2014. "Predicting the Risk of Suicide by Analyzing the Text of Clinical Notes," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-7, January.
    • Handle: RePEc:plo:pone00:0085733
    DOI: 10.1371/journal.pone.0085733
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    References listed on IDEAS

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    1. Kaplan, M.S. & McFarland, B.H. & Huguet, N. & Valenstein, M., 2012. "Suicide risk and precipitating circumstances among young, middle-aged, and older male veterans," American Journal of Public Health, American Public Health Association, vol. 102(S1), pages 131-137.
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    Cited by:

    1. Joanna F Dipnall & Julie A Pasco & Michael Berk & Lana J Williams & Seetal Dodd & Felice N Jacka & Denny Meyer, 2016. "Into the Bowels of Depression: Unravelling Medical Symptoms Associated with Depression by Applying Machine-Learning Techniques to a Community Based Population Sample," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-19, December.

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