Abstract
We developed linguistics-driven prediction models to estimate the risk of suicide. These models were generated from unstructured clinical notes taken from a national sample of U.S. Veterans Administration (VA) medical records. We created three matched cohorts: veterans who committed suicide, veterans who used mental health services and did not commit suicide, and veterans who did not use mental health services and did not commit suicide during the observation period (n = 70 in each group). From the clinical notes, we generated datasets of single keywords and multi-word phrases, and constructed prediction models using a machine-learning algorithm based on a genetic programming framework. The resulting inference accuracy was consistently 65% or more. Our data therefore suggests that computerized text analytics can be applied to unstructured medical records to estimate the risk of suicide. The resulting system could allow clinicians to potentially screen seemingly healthy patients at the primary care level, and to continuously evaluate the suicide risk among psychiatric patients.
Highlights
Detecting individuals who are at increased risk of suicide is a major clinical challenge
Suicide among military personnel and veterans is a topic of international concern, and the U.S Veterans Health Administration (VHA) has increasingly focused on suicide prevention [1,2]
Registry to obtain a random sample of 100 VHA enrollees who committed suicide in 2009
Summary
Detecting individuals who are at increased risk of suicide is a major clinical challenge. While suicidality is a prominent risk factor for suicide attempts and completions, only approximately 30% of patients attempting suicide disclose their suicidal ideation [4,5,6], and the vast majority of individuals who express suicidal ideation never go on to attempt suicide [7,8,9]. Given this poor predictive value, clinicians might consider a more comprehensive approach by evaluating additional demographic risk factors for suicide
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