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Original Investigation |

Improving Prediction of Suicide and Accidental Death After Discharge From General Hospitals With Natural Language Processing ONLINE FIRST

Thomas H. McCoy Jr, MD1; Victor M. Castro, MS1; Ashlee M. Roberson, BA1; Leslie A. Snapper, BS1; Roy H. Perlis, MD, MS1
[+] Author Affiliations
1Center for Experimental Drugs and Diagnostics, Center for Human Genetic Research and Department of Psychiatry, Massachusetts General Hospital, Boston
JAMA Psychiatry. Published online September 14, 2016. doi:10.1001/jamapsychiatry.2016.2172
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Importance  Suicide represents the 10th leading cause of death across age groups in the United States (12.6 cases per 100 000) and remains challenging to predict. While many individuals who die by suicide are seen by physicians before their attempt, they may not seek psychiatric care.

Objective  To determine the extent to which incorporating natural language processing of narrative discharge notes improves stratification of risk for death by suicide after medical or surgical hospital discharge.

Design, Setting, and Participants  In this retrospective health care use study, clinical data were analyzed from individuals with discharges from 2 large academic medical centers between January 1, 2005, and December 31, 2013.

Main Outcomes and Measures  The primary outcome was suicide as a reported cause of death based on Massachusetts Department of Public Health records. Regression models for prediction of death by suicide or accidental death were compared relying solely on coded clinical data and those using natural language processing of hospital discharge notes.

Results  There were 845 417 hospital discharges represented in the cohort, including 458 053 unique individuals. Overall, all-cause mortality was 18% during 9 years, and the median follow-up was 5.2 years. The cohort included 235 (0.1%) who died by suicide during 2.4 million patient-years of follow-up. Positive valence reflected in narrative notes was associated with a 30% reduction in risk for suicide in models adjusted for coded sociodemographic and clinical features (hazard ratio, 0.70; 95% CI, 0.58-0.85; P < .001) and improved model fit (χ22 = 14.843, P < .001 by log-likelihood test). The C statistic was 0.741 (95% CI, 0.738-0.744) for models of suicide with or without inclusion of accidental death.

Conclusions and Relevance  Multiple clinical features available at hospital discharge identified a cohort of individuals at substantially increased risk for suicide. Greater positive valence expressed in narrative discharge summaries was associated with substantially diminished risk. Automated tools to aid clinicians in evaluating these risks may assist in identifying high-risk individuals.

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Figure 1.
Kaplan-Meier Curves for Time to Death by Suicide Among 458 053 Individuals With at Least 1 Hospital Discharge by Predicted Risk Quartile

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Figure 2.
Kaplan-Meier Curves for Time to Death by Suicide or Accidental Death Among 458 053 Individuals With at Least 1 Hospital Discharge by Predicted Risk Quartile

The axes are rescaled inside the figure to improve interpretability.

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