TY  - JOUR
PY  - 2020//
TI  - Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records
JO  - Translational psychiatry
A1  - Zheng, Le
A1  - Wang, Oliver
A1  - Hao, Shiying
A1  - Ye, Chengyin
A1  - Liu, Modi
A1  - Xia, Minjie
A1  - Sabo, Alex N.
A1  - Markovic, Liliana
A1  - Stearns, Frank
A1  - Kanov, Laura
A1  - Sylvester, Karl G.
A1  - Widen, Eric
A1  - McElhinney, Doff B.
A1  - Zhang, Wei
A1  - Liao, Jiayu
A1  - Ling, Xuefeng B.
SP  - e72
EP  - e72
VL  - 10
IS  - 1
N2  - Suicide is the tenth leading cause of death in the United States (US). An early-warning system (EWS) for suicide attempt could prove valuable for identifying those at risk of suicide attempts, and analyzing the contribution of repeated attempts to the risk of eventual death by suicide. In this study we sought to develop an EWS for high-risk suicide attempt patients through the development of a population-based risk stratification surveillance system. Advanced machine-learning algorithms and deep neural networks were utilized to build models with the data from electronic health records (EHRs). A final risk score was calculated for each individual and calibrated to indicate the probability of a suicide attempt in the following 1-year time period. Risk scores were subjected to individual-level analysis in order to aid in the interpretation of the results for health-care providers managing the at-risk cohorts. The 1-year suicide attempt risk model attained an area under the curve (AUC ROC) of 0.792 and 0.769 in the retrospective and prospective cohorts, respectively. The suicide attempt rate in the "very high risk" category was 60 times greater than the population baseline when tested in the prospective cohorts. Mental health disorders including depression, bipolar disorders and anxiety, along with substance abuse, impulse control disorders, clinical utilization indicators, and socioeconomic determinants were recognized as significant features associated with incident suicide attempt.<p />  <p>Language: en</p>
LA  - en
SN  - 2158-3188
UR  - http://dx.doi.org/10.1038/s41398-020-0684-2
ID  - ref1
ER  -