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

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Poulin:2014:PLoSONE,
  author =       "Chris Poulin and Brian Shiner and Paul Thompson and 
                 Linas Vepstas and Yinong Young-Xu and 
                 Benjamin Goertzel and Bradley Watts and Laura Flashman and 
                 Thomas McAllister",
  title =        "Predicting the Risk of Suicide by Analyzing the Text
                 of Clinical Notes",
  journal =      "PLoS ONE",
  year =         "2014",
  volume =       "9",
  number =       "1",
  pages =        "e85733",
  month =        jan # " 28",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3904866",
  URL =          "http://www.ncbi.nlm.nih.gov/pubmed/24489669",
  URL =          "http://dx.doi.org/10.1371/journal.pone.0085733",
  DOI =          "doi:10.1371/journal.pone.0085733",
  size =         "7 pages",
  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 65percent or more. Our data therefore
                 suggests that computerised 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.",
  notes =        "'This report describes the suicidality prediction
                 models created under the DARPA DCAPS program in
                 association with the Durkheim Project
                 http://durkheimproject.org/' from
                 http://arxiv.org/abs/1310.6775

                 'CORRECTION: The first sentence of the Competing
                 interests statement is incorrect. The Competing
                 interests statement should read: CP is President of
                 Patterns and Predictions, whose company in conjunction
                 with PT and LV, has a patent pending in relation to
                 information discussed in the Appendix 1. There are no
                 further patents, products in development or marketed
                 products to declare. This does not alter our adherence
                 to all of the PLOS ONE policies on sharing data and
                 materials.' Published: March 20, 2014",
}

Genetic Programming entries for Chris Poulin Brian Shiner Paul Thompson Linas Vepstas Yinong Young-Xu Ben Goertzel Bradley Watts Laura Flashman Thomas McAllister

Citations