TY  - JOUR
PY  - 2023//
TI  - Performance of a prediction model of suicide attempts across race and ethnicity
JO  - JAMA Psychiatry
A1  - Papini, Santiago
A1  - Hsin, Honor
A1  - Kipnis, Patricia
A1  - Liu, Vincent X.
A1  - Lu, Yun
A1  - Sterling, Stacy A.
A1  - Iturralde, Esti
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - Innovative prevention strategies are needed to reduce US suicide rates, which have been steadily increasing with a recent disproportionate increase among Black and Hispanic populations.1 Predictive models of suicide risk have been developed using machine learning with electronic health records (EHRs).2 Some models achieve the performance needed to cost-effectively target high-risk individuals.3 However, recent work applying a suicide-death prediction model found excellent performance at the population level (indexed by area under the receiver operating characteristic curve [AUC] = 0.82), but much lower AUC for the American Indian or Alaskan Native subsample (AUC = 0.60).4 We examined whether similar disparities exist in the prediction of suicide attempts, which are far more common and have detrimental effects on individuals and health care systems, even when nonfatal.<p />  <p>Language: en</p>
LA  - en
SN  - 2168-622X
UR  - http://dx.doi.org/10.1001/jamapsychiatry.2022.5063
ID  - ref1
ER  -