Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2016 Oct 1;73(10):1064-1071.
doi: 10.1001/jamapsychiatry.2016.2172.

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

Thomas H McCoy Jr  1 , Victor M Castro  1 , Ashlee M Roberson  1 , Leslie A Snapper  1 , Roy H Perlis  1
Affiliations

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

Thomas H McCoy Jr et al. JAMA Psychiatry. .

Abstract

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.

PubMed Disclaimer

Conflict of interest statement

Conflict of Interest Disclosures: Dr Perlis reported serving on scientific advisory boards or consulting for Genomind LLC, Healthrageous, Pfizer, Perfect Health, Proteus Biomedical, PsyBrain Inc, and RID Ventures LLC and reported receiving royalties through Massachusetts General Hospital from Concordant Rater Systems (now Bracket/Medco). No other disclosures were reported.

Figures

Figure 1.
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
The axes are rescaled inside the figure to improve interpretability.
Figure 2.
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.
See this image and copyright information in PMC

References

    1. National Vital Statistics System, National Centerfor Health Statistics, Centers for Disease Control and Prevention. 10 Leading causes of death by age group, United States: 2013. Produced by National Center for Injury Prevention and Control using WISQARS. www.cdc.gov/injury/wisqars/pdf/leading_causes_of_death_by_age_group_2013.... Accessed August 3, 2016.
    1. Xu J, Murphy SL, Kochanek KD, Bastian BA. Deaths: final data for 2013. Natl Vital Stat Rep. 2016;64(2):1–119. - PubMed
    1. Borges G, Nock MK, Haro Abad JM, et al. Twelve-month prevalence of and risk factors for suicide attempts in the World Health Organization World Mental Health Surveys. J Clin Psychiatry. 2010;71(12):1617–1628. - PMC - PubMed
    1. Nock MK, Hwang I, Sampson NA, Kessler RC. Mental disorders, comorbidity and suicidal behavior: results from the National Comorbidity Survey Replication. Mol Psychiatry. 2010;15(8):868–876. - PMC - PubMed
    1. Nock MK, Green JG, Hwang I, et al. Prevalence, correlates, and treatment of lifetime suicidal behavior among adolescents: results from the National Comorbidity Survey Replication Adolescent Supplement. JAMA Psychiatry. 2013;70 (3):300–310. - PMC - PubMed

MeSH terms