TY  - JOUR
PY  - 2024//
TI  - High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning
JO  - Scientific reports
A1  - Dhaubhadel, Sayera
A1  - Ganguly, Kumkum
A1  - Ribeiro, Ruy M.
A1  - Cohn, Judith D.
A1  - Hyman, James M.
A1  - Hengartner, Nicolas W.
A1  - Kolade, Beauty
A1  - Singley, Anna
A1  - Bhattacharya, Tanmoy
A1  - Finley, Patrick
A1  - Levin, Drew
A1  - Thelen, Haedi
A1  - Cho, Kelly
A1  - Costa, Lauren
A1  - Ho, Yuk-Lam
A1  - Justice, Amy C.
A1  - Pestian, John
A1  - Santel, Daniel
A1  - Zamora-Resendiz, Rafael
A1  - Crivelli, Silvia
A1  - Tamang, Suzanne
A1  - Martins, Susana
A1  - Trafton, Jodie
A1  - Oslin, David W.
A1  - Beckham, Jean C.
A1  - Kimbrel, Nathan A.
A1  - McMahon, Benjamin H.
SP  - e1793
EP  - e1793
VL  - 14
IS  - 1
N2  - We present an ensemble transfer learning method to predict suicide from Veterans Affairs (VA) electronic medical records (EMR). A diverse set of base models was trained to predict a binary outcome constructed from reported suicide, suicide attempt, and overdose diagnoses with varying choices of study design and prediction methodology. Each model used twenty cross-sectional and 190 longitudinal variables observed in eight time intervals covering 7.5 years prior to the time of prediction. Ensembles of seven base models were created and fine-tuned with ten variables expected to change with study design and outcome definition in order to predict suicide and combined outcome in a prospective cohort. The ensemble models achieved c-statistics of 0.73 on 2-year suicide risk and 0.83 on the combined outcome when predicting on a prospective cohort of [Formula: see text] 4.2 M veterans. The ensembles rely on nonlinear base models trained using a matched retrospective nested case-control (Rcc) study cohort and show good calibration across a diversity of subgroups, including risk strata, age, sex, race, and level of healthcare utilization. In addition, a linear Rcc base model provided a rich set of biological predictors, including indicators of suicide, substance use disorder, mental health diagnoses and treatments, hypoxia and vascular damage, and demographics.<p />  <p>Language: en</p>
LA  - en
SN  - 2045-2322
UR  - http://dx.doi.org/10.1038/s41598-024-51762-9
ID  - ref1
ER  -