Predicting the risk of suicide by analyzing the text of clinical notes.

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, veteran...

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Autores principales:	Chris Poulin, Brian Shiner, Paul Thompson, Linas Vepstas, Yinong Young-Xu, Benjamin Goertzel, Bradley Watts, Laura Flashman, Thomas McAllister
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f06d40e8e81b47cb9a769b49ccc518ec
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spelling	oai:doaj.org-article:f06d40e8e81b47cb9a769b49ccc518ec2021-11-18T08:35:27ZPredicting the risk of suicide by analyzing the text of clinical notes.1932-620310.1371/journal.pone.0085733https://doaj.org/article/f06d40e8e81b47cb9a769b49ccc518ec2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24489669/?tool=EBIhttps://doaj.org/toc/1932-6203We 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.Chris PoulinBrian ShinerPaul ThompsonLinas VepstasYinong Young-XuBenjamin GoertzelBradley WattsLaura FlashmanThomas McAllisterPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 1, p e85733 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Chris Poulin Brian Shiner Paul Thompson Linas Vepstas Yinong Young-Xu Benjamin Goertzel Bradley Watts Laura Flashman Thomas McAllister Predicting the risk of suicide by analyzing the text of clinical notes.
description	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.
format	article
author	Chris Poulin Brian Shiner Paul Thompson Linas Vepstas Yinong Young-Xu Benjamin Goertzel Bradley Watts Laura Flashman Thomas McAllister
author_facet	Chris Poulin Brian Shiner Paul Thompson Linas Vepstas Yinong Young-Xu Benjamin Goertzel Bradley Watts Laura Flashman Thomas McAllister
author_sort	Chris Poulin
title	Predicting the risk of suicide by analyzing the text of clinical notes.
title_short	Predicting the risk of suicide by analyzing the text of clinical notes.
title_full	Predicting the risk of suicide by analyzing the text of clinical notes.
title_fullStr	Predicting the risk of suicide by analyzing the text of clinical notes.
title_full_unstemmed	Predicting the risk of suicide by analyzing the text of clinical notes.
title_sort	predicting the risk of suicide by analyzing the text of clinical notes.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/f06d40e8e81b47cb9a769b49ccc518ec
work_keys_str_mv	AT chrispoulin predictingtheriskofsuicidebyanalyzingthetextofclinicalnotes AT brianshiner predictingtheriskofsuicidebyanalyzingthetextofclinicalnotes AT paulthompson predictingtheriskofsuicidebyanalyzingthetextofclinicalnotes AT linasvepstas predictingtheriskofsuicidebyanalyzingthetextofclinicalnotes AT yinongyoungxu predictingtheriskofsuicidebyanalyzingthetextofclinicalnotes AT benjamingoertzel predictingtheriskofsuicidebyanalyzingthetextofclinicalnotes AT bradleywatts predictingtheriskofsuicidebyanalyzingthetextofclinicalnotes AT lauraflashman predictingtheriskofsuicidebyanalyzingthetextofclinicalnotes AT thomasmcallister predictingtheriskofsuicidebyanalyzingthetextofclinicalnotes
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Predicting the risk of suicide by analyzing the text of clinical notes.

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