Suicide Risk Modeling with Uncertain Diagnostic Records

by   Wenjie Wang, et al.

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.


page 1

page 2

page 3

page 4


Machine learning using longitudinal prescription and medical claims for the detection of nonalcoholic steatohepatitis (NASH)

Objectives To develop and evaluate machine learning models to detect sus...

Transformer-based unsupervised patient representation learning based on medical claims for risk stratification and analysis

The claims data, containing medical codes, services information, and inc...

Medical Concept Representation Learning from Claims Data and Application to Health Plan Payment Risk Adjustment

Risk adjustment has become an increasingly important tool in healthcare....

Predicting colorectal polyp recurrence using time-to-event analysis of medical records

Identifying patient characteristics that influence the rate of colorecta...

Covid-19 risk factors: Statistical learning from German healthcare claims data

We analyse prior risk factors for severe, critical or fatal courses of C...

Explainable Graph Neural Network for Alzheimer's Disease And Related Dementias Risk Prediction

Alzheimer's disease and related dementias (ADRD) ranks as the sixth lead...

Please sign up or login with your details

Forgot password? Click here to reset