Predicting Suicidal Behavior From Longitudinal Electronic Health Records
Top Cited Papers
- 1 February 2017
- journal article
- research article
- Published by American Psychiatric Association Publishing in American Journal of Psychiatry
- Vol. 174 (2), 154-162
- https://doi.org/10.1176/appi.ajp.2016.16010077
Abstract
The purpose of this article was to determine whether longitudinal historical data, commonly available in electronic health record (EHR) systems, can be used to predict patients’ future risk of suicidal behavior. Bayesian models were developed using a retrospective cohort approach. EHR data from a large health care database spanning 15 years (1998–2012) of inpatient and outpatient visits were used to predict future documented suicidal behavior (i.e., suicide attempt or death). Patients with three or more visits (N=1,728,549) were included. ICD-9-based case definition for suicidal behavior was derived by expert clinician consensus review of 2,700 narrative EHR notes (from 520 patients), supplemented by state death certificates. Model performance was evaluated retrospectively using an independent testing set. Among the study population, 1.2% (N=20,246) met the case definition for suicidal behavior. The model achieved sensitive (33%–45% sensitivity), specific (90%−95% specificity), and early (3–4 years in advance on average) prediction of patients’ future suicidal behavior. The strongest predictors identified by the model included both well-known (e.g., substance abuse and psychiatric disorders) and less conventional (e.g., certain injuries and chronic conditions) risk factors, indicating that a data-driven approach can yield more comprehensive risk profiles. Longitudinal EHR data, commonly available in clinical settings, can be useful for predicting future risk of suicidal behavior. This modeling approach could serve as an early warning system to help clinicians identify high-risk patients for further screening. By analyzing the full phenotypic breadth of the EHR, computerized risk screening approaches may enhance prediction beyond what is feasible for individual clinicians.Keywords
This publication has 22 references indexed in Scilit:
- Incident user cohort study of risk for gastrointestinal bleed and stroke in individuals with major depressive disorder treated with antidepressantsBMJ Open, 2012
- Area Disease Estimation Based on Sentinel Hospital RecordsPLOS ONE, 2011
- Identification of hospitalizations for intentional self‐harm when E‐codes are incompletely recordedPharmacoepidemiology and Drug Safety, 2010
- Measuring the impact of health policies using Internet search patterns: the case of abortionBMC Public Health, 2010
- Exploratory Data Mining Analysis Identifying Subgroups of Patients With Depression Who Are at High Risk for SuicideBritish Journal of Psychology, 2009
- Longitudinal histories as predictors of future diagnoses of domestic abuse: modelling studyBMJ, 2009
- Suicide and Suicidal BehaviorEpidemiologic Reviews, 2008
- Cross-national prevalence and risk factors for suicidal ideation, plans and attemptsThe British Journal of Psychiatry, 2008
- An Epidemiological Network Model for Disease Outbreak DetectionPLoS Medicine, 2007
- Syndromic surveillance: the effects of syndrome grouping on model accuracy and outbreak detectionAnnals of Emergency Medicine, 2004