@article{ref1,
title="Identifying suicidal ideation and attempt from clinical notes within a large integrated health care system",
journal="Permanente journal",
year="2022",
author="Xie, Fagen and Ling Grant, Deborah S. and Chang, John and Amundsen, Britta I. and Hechter, Rulin C.",
volume="26",
number="1",
pages="85-93",
abstract="PURPOSE: The purpose of this study was to develop a natural language processing algorithm to identify suicidal ideation/attempt from free-text clinical notes. <br><br>METHODS: Clinical notes containing prespecified keywords related to suicidal ideation/attempts from 2010 to 2018 were extracted from our organization's electronic health record system. A random sample of 864 clinical notes was selected and equally divided into 4 subsets. These subsets were reviewed and classified as 1 of the following 3 suicidal ideation/attempt categories (current, historical, and no) by experienced research chart abstractors. The first 3 data sets were used to develop the rule-based computerized algorithm sequentially and the fourth data set was used to evaluate the algorithm's performance. The validated algorithm was then applied to the entire study sample of clinical notes. <br><br>RESULTS: The computerized algorithm correctly identified 23 of the 26 confirmed current suicidal ideation/attempts and all 10 confirmed historical suicidal ideation/attempts in the validation data set. It produced an 88.5% sensitivity and a 100.0% positive predictive value for current suicidal ideation/attempts, and a 100.0% sensitivity and positive predictive value for historical suicidal ideation/attempts. After applying the computerized algorithm to the entire set of study notes, we identified a total of 1,050,287 current ideation/attempt events and 293,037 historical ideation/attempt events documented in clinical notes. Those for which current ideation/attempt events were documented were more likely to be female (59.5%), 25-44 years old (28.3%), and White (43.4%). <br><br>CONCLUSION: Our study demonstrated that a computerized algorithm can effectively identify suicidal ideation/attempts from clinical notes. This algorithm can be utilized in support of suicide prevention research programs and patient care quality improvement initiatives.<p /> <p>Language: en</p>",
language="en",
issn="1552-5767",
doi="10.7812/TPP/21.102",
url="http://dx.doi.org/10.7812/TPP/21.102"
}