Motivated by the pressing need for suicide prevention through improving
behavioral healthcare, we use medical claims data to study the risk of
subsequent suicide attempts for patients who were hospitalized due to suicide
attempts and later discharged. Understanding the risk behaviors of such
patients at elevated suicide risk is an important step towards the goal of
"Zero Suicide". An immediate and unconventional challenge is that the
identification of suicide attempts from medical claims contains substantial
uncertainty: almost 20\% of "suspected" suicide attempts are identified from
diagnostic codes indicating external causes of injury and poisoning with
undermined intent. It is thus of great interest to learn which of these
undetermined events are more likely actual suicide attempts and how to properly
utilize them in survival analysis with severe censoring. To tackle these
interrelated problems, we develop an integrative Cox cure model with
regularization to perform survival regression with uncertain events and a
latent cure fraction. We apply the proposed approach to study the risk of
subsequent suicide attempt after suicide-related hospitalization for adolescent
and young adult population, using medical claims data from Connecticut. The
identified risk factors are highly interpretable; more intriguingly, our method
distinguishes the risk factors that are most helpful in assessing either
susceptibility or timing of subsequent attempt. The predicted statuses of the
uncertain attempts are further investigated, leading to several new insights on
suicide event identification