@article{ref1,
title="Machine learning to differentiate risk of suicide attempt and self-harm after general medical hospitalization of women with mental illness",
journal="Medical care",
year="2021",
author="Brooks, John O. 3rd and Pathak, Jyotishman and Thiruvalluru, Rohith and Edgcomb, Juliet B.",
volume="59",
number="",
pages="S58-S64",
abstract="BACKGROUND: Suicide prevention is a public health priority, but risk factors for suicide after medical hospitalization remain understudied. This problem is critical  for women, for whom suicide rates in the United States are disproportionately  increasing. <br><br>OBJECTIVE: To differentiate the risk of suicide attempt and self-harm  following general medical hospitalization among women with depression, bipolar  disorder, and chronic psychosis. <br><br>METHODS: We developed a machine learning algorithm  that identified risk factors of suicide attempt and self-harm after general  hospitalization using electronic health record data from 1628 women in the  University of California Los Angeles Integrated Clinical and Research Data  Repository. To assess replicability, we applied the algorithm to a larger sample of  140,848 women in the New York City Clinical Data Research Network. <br><br>RESULTS: The  classification tree algorithm identified risk groups in University of California Los  Angeles Integrated Clinical and Research Data Repository (area under the curve 0.73,  sensitivity 73.4, specificity 84.1, accuracy 0.84), and predictor combinations  characterizing key risk groups were replicated in New York City Clinical Data  Research Network (area under the curve 0.71, sensitivity 83.3, specificity 82.2, and  accuracy 0.84). Predictors included medical comorbidity, history of  pregnancy-related mental illness, age, and history of suicide-related behavior. Women with antecedent medical illness and history of pregnancy-related mental  illness were at high risk (6.9%-17.2% readmitted for suicide-related behavior), as  were women below 55 years old without antecedent medical illness (4.0%-7.5%  readmitted). <br><br>CONCLUSIONS: Prevention of suicide attempt and self-harm among women  following acute medical illness may be improved by screening for sex-specific  predictors including perinatal mental health history.<p /> <p>Language: en</p>",
language="en",
issn="0025-7079",
doi="10.1097/MLR.0000000000001467",
url="http://dx.doi.org/10.1097/MLR.0000000000001467"
}