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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. [ABSTRACT FROM AUTHOR]

Sociological methods have consistently succeeded while econometric methods have failed in research on capital punishment and homicide. But econometricians aggressively promote their findings in public policy venues, while sociologists are less assertive. This is due to cultural differences between the disciplines, and to a philosophy of science that values falsification of hypotheses over progress in answering research questions. This problem has occurred and is likely to reoccur in other policy areas where sociologists are insufficiently assertive in defending their accomplishments. [ABSTRACT FROM AUTHOR]