@article{ref1,
title="Using natural language processing to classify suicide notes",
journal="AMIA annual symposium proceedings",
year="2008",
author="Pestian, John P. and Matykiewicz, Pawel and Grupp-Phelan, Jacqueline and Arszman Lavanier Ma, S and Combs, Jennifer and Kowatch, Robert",
volume="",
number="",
pages="1091-1091",
abstract="We hypothesize that machine-learning algorithms (MLA) could classify completer and ideator suicide notes as well a mental health professionals (MHP). Five MHPs classified 66 notes as either ideator or completer; machine learning algorithms (MLA) were used for the same task. Results: MHPs were accurate 71% of the time; the SMO algorithm was accurate 79% of the time. This is an important first step in developing an evidence based suicide predictor for emergency department use.<p /> <p>Language: en</p>",
language="en",
issn="1559-4076",
doi="",
url="http://dx.doi.org/"
}