학술논문
High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning.
Document Type
Article
Author
Dhaubhadel, Sayera; Ganguly, Kumkum; Ribeiro, Ruy M.; Cohn, Judith D.; Hyman, James M.; Hengartner, Nicolas W.; Kolade, Beauty; Singley, Anna; Bhattacharya, Tanmoy; Finley, Patrick; Levin, Drew; Thelen, Haedi; Cho, Kelly; Costa, Lauren; Ho, Yuk-Lam; Justice, Amy C.; Pestian, John; Santel, Daniel; Zamora-Resendiz, Rafael; Crivelli, Silvia
Source
Subject
*SUICIDE risk factors
*MENTAL health services
*ATTEMPTED suicide
*VETERANS
*ELECTRONIC health records
*PLATELET-rich plasma
*
*
*
*
*
Language
ISSN
2045-2322
Abstract
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 ∼ 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. [ABSTRACT FROM AUTHOR]