High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning.

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 combine

SUBMITTER: Dhaubhadel S 

PROVIDER: S-EPMC10799879 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature