TY  - JOUR
PY  - 2021//
TI  - Applying machine learning approaches to suicide prediction using healthcare data: overview and future directions
JO  - Frontiers in psychiatry
A1  - Boudreaux, Edwin D.
A1  - Rundensteiner, Elke
A1  - Liu, Feifan
A1  - Wang, Bo
A1  - Larkin, Celine
A1  - Agu, Emmanuel
A1  - Ghosh, Samiran
A1  - Semeter, Joshua
A1  - Simon, Gregory
A1  - Davis-Martin, Rachel E.
SP  - e707916
EP  - e707916
VL  - 12
IS  - 
N2  - OBJECTIVE: Early identification of individuals who are at risk for suicide is crucial in supporting suicide prevention. Machine learning is emerging as a promising approach to support this objective. Machine learning is broadly defined as a set of mathematical models and computational algorithms designed to automatically learn complex patterns between predictors and outcomes from example data, without being explicitly programmed to do so. The model's performance continuously improves over time by learning from newly available data. <br><br>METHOD: This concept paper explores how machine learning approaches applied to healthcare data obtained from electronic health records, including billing and claims data, can advance our ability to accurately predict future suicidal behavior. <br><br>RESULTS: We provide a general overview of machine learning concepts, summarize exemplar studies, describe continued challenges, and propose innovative research directions. <br><br>CONCLUSION: Machine learning has potential for improving estimation of suicide risk, yet important challenges and opportunities remain. Further research can focus on incorporating evolving methods for addressing data imbalances, understanding factors that affect generalizability across samples and healthcare systems, expanding the richness of the data, leveraging newer machine learning approaches, and developing automatic learning systems.<p />  <p>Language: en</p>
LA  - en
SN  - 1664-0640
UR  - http://dx.doi.org/10.3389/fpsyt.2021.707916
ID  - ref1
ER  -