Research Domain Criteria scores estimated through natural language processing are associated with risk for suicide and accidental death.

ABSTRACT: BACKGROUND:Identification of individuals at increased risk for suicide is an important public health priority, but the extent to which considering clinical phenomenology improves prediction of longer term outcomes remains understudied. Hospital discharge provides an opportunity to stratify risk using readily available clinical records and details. METHODS:We applied a validated natural language processing tool to generate estimated Research Domain Criteria (RDoC) scores for a cohort of 444,317 individuals drawn from 815,457 hospital discharges between 2005 and 2013. We used survival analysis to examine the association of this risk with suicide and accidental death, adjusted for sociodemographic features. RESULTS:In adjusted models, symptoms in each of the five domains contributed to increm

SUBMITTER: McCoy TH 

PROVIDER: S-EPMC6488379 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature