The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Predicting Suicide Attempts and Suicide Deaths Following Outpatient Visits Using Electronic Health Records
Open accessAbstract
OBJECTIVE: The authors sought to develop and validate models using electronic health records to predict suicide attempt and suicide death following an outpatient visit. METHOD: Across seven health systems, 2,960,929 patients age 13 or older (mean age, 46 years; 62% female) made 10,275,853 specialty mental health visits and 9,685,206 primary care visits with mental health diagnoses between Jan. 1, 2009, and June 30, 2015. Health system records and state death certificate data identified suicide attempts (N=24,133) and suicide deaths (N=1,240) over 90 days following each visit. Potential predictors included 313 demographic and clinical characteristics extracted from records for up to 5 years before each visit: prior suicide attempts, mental health and substance use diagnoses, medical diagnoses, psychiatric medications dispensed, inpatient or emergency department care, and routinely administered depression questionnaires. Logistic regression models predicting suicide attempt and death were developed using penalized LASSO (least absolute shrinkage and selection operator) variable selection in a random sample of 65% of the visits and validated in the remaining 35%. RESULTS: Mental health specialty visits with risk scores in the top 5% accounted for 43% of subsequent suicide attempts and 48% of suicide deaths. Of patients scoring in the top 5%, 5.4% attempted suicide and 0.26% died by suicide within 90 days. C-statistics (equivalent to area under the curve) for prediction of suicide attempt and suicide death were 0.851 (95% CI=0.848, 0.853) and 0.861 (95% CI=0.848, 0.875), respectively. Primary care visits with scores in the top 5% accounted for 48% of subsequent suicide attempts and 43% of suicide deaths. C-statistics for prediction of suicide attempt and suicide death were 0.853 (95% CI=0.849, 0.857) and 0.833 (95% CI=0.813, 0.853), respectively. CONCLUSIONS: Prediction models incorporating both health record data and responses to self-report questionnaires substantially outperform existing suicide risk prediction tools.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“One study of machine learning of health records for the prediction of suicide and suicide attempts examined the medical and psychiatric records of ~3 million patients with at least one mental health diagnosis across seven different health care systems 222 .”
From Suicide and suicide risk (2019) · cites this paper as [222]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In this study, an algorithm was developed that could identify 5% of the population that accounted for 43-48% of the suicides and suicide attempts, and was able to predict suicide and suicide attempts in the next 90 days with a specificity of ~70% and a sensitivity of ~80% in both specialty mental health and primary care settings 222 .”
From Suicide and suicide risk (2019) · cites this paper as [222]
Experimental — accuracy not yet measuredIn the citing paper
“multiple research groups have developed suicide risk prediction models using electronic health record data.10,11,12,13”
From Accuracy requirements for cost-effective suicide risk prediction among primary care patients in the US (2021) · cites this paper as [12]
In the citing paper
“and 2% to 6%11,12 for predicting suicide attempts”
From Accuracy requirements for cost-effective suicide risk prediction among primary care patients in the US (2021) · cites this paper as [12]
In the citing paper
“The corresponding PPVs were 0.1% to 0.3% for predicting suicide death12,13”
From Accuracy requirements for cost-effective suicide risk prediction among primary care patients in the US (2021) · cites this paper as [12]
In the citing paper
“48%, from a model developed in a multistate collaboration.12”
From Accuracy requirements for cost-effective suicide risk prediction among primary care patients in the US (2021) · cites this paper as [12]
In the citing paper
“even higher PPVs (>10%)12 could be attained by increasing specificity above 99%, although with a consequent decrease in the proportion of cases identified.”
From Accuracy requirements for cost-effective suicide risk prediction among primary care patients in the US (2021) · cites this paper as [12]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927
Kass, R. E., & Raftery, A. E. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476572 · Cited by 12,558
The use of the area under the ROC curve in the evaluation of machine learning algorithms
Bradley, A. P. (1997) · Pattern Recognition · doi:10.1016/s0031-3203(96)00142-2 · Cited by 7,217
Validation of a combined comorbidity index
Charlson, M., Szatrowski, T. P., Peterson, J., & Gold, J. (1994) · Journal of Clinical Epidemiology · doi:10.1016/0895-4356(94)90129-5 · Cited by 6,587
Posner, K., Brown, G. K., Stanley, B., Brent, D. A., Yershova, K. V., Oquendo, M. A., Currier, G. W., Melvin, G. A., Greenhill, L., Shen, S., et al. (2011) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2011.10111704 · Cited by 5,157
Kroenke, K., Spitzer, R. L., Williams, J. B. W., & Löwe, B. (2010) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2010.03.006 · Cited by 4,057
Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research
Franklin, J. C., Ribeiro, J. D., Fox, K. R., Bentley, K. H., Kleiman, E. M., Huang, X., Musacchio, K. M., Jaroszewski, A. C., Chang, B. P., & Nock, M. K. (2017) · Psychological Bulletin · doi:10.1037/bul0000084 · Cited by 3,784
Flagged first, then by citations
Research works printed but not linked: 1
- Ross TR, 2014, EGEMS (Wash DC), 2, 1049
Cited by
Sorted by citations
Items 201–300 of 400
Suicide after contact with a national digital mental health service
Nielssen, O. B., Staples, L., Ryan, K., Karin, E., Kayrouz, R., Dear, B. F., Cross, S. P. M., & Titov, N. (2022) · Internet Interventions · doi:10.1016/j.invent.2022.100516 · Cited by 8
Effects of Geography on Risk for Future Suicidal Ideation and Attempts Among Children and Youth
Xi, W., Banerjee, S., Zima, B. T., Alexopoulos, G. S., Olfson, M., Xiao, Y., & Pathak, J. (2023) · JAACAP Open · doi:10.1016/j.jaacop.2023.07.002 · Cited by 8
“18 To ensure that the predictors were valid in our study population, we adopted a study design similar to that in the previous study 16 by restricting the cohort to those individuals who had at least 1 outpatient care encounter for mental health or substance use disorder (MH/SUD).”
BackgroundMethodStability of Suicide Risk Prediction Models During Changes in Health Care Delivery
Simon, G. E., Cruz, M., Shortreed, S. M., Sterling, S., Coleman, K. J., Ahmedani, B. K., Yaseen, Z. S., & Mosholder, A. D. (2023) · Psychiatric Services · doi:10.1176/appi.ps.20230172 · Cited by 8
Self-citationDisclosure of suicidal thoughts and behaviors: The impact of suicide event type
Ammerman, B. A., Park, Y., & O’Loughlin, C. M. (2022) · Journal of Clinical Psychology · doi:10.1002/jclp.23357 · Cited by 10
Communicating Precision Medicine Research: Multidisciplinary Teams and Diverse Communities
Beans, J. A., Trinidad, S. B., Blacksher, E. A., Hiratsuka, V. Y., Spicer, P., Woodahl, E. L., Boyer, B. B., Lewis, C. M., Gaffney, P. M., Garrison, N. A., et al. (2022) · Public Health Genomics · doi:10.1159/000525684 · Cited by 8
Dolsen, E. A., Nishimi, K. M., LeWinn, K. Z., Byers, A. L., Tripp, P., Woodward, E., Khan, A. J., Marx, B. P., Borsari, B. E., Jiha, A., et al. (2022) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2022.10.009 · Cited by 8
“Studies focused on suicide risk have made use of these methods to move beyond single-domain hypotheses (Simon et al., 2018; van Mens et al., 2020; Walsh et al., 2017).”
Maple, M. J., Wayland, S., Pearce, T., Sanford, R., & Bhullar, N. (2022) · Community Mental Health Journal · doi:10.1007/s10597-022-00978-y · Cited by 7
“The provision of evidence-based aftercare services can reduce this shortterm risk, and more services are becoming available (Simon et al., 2018), often focused on the immediate post-discharge period.”
BackgroundKristóf, Z., Gál, Z., Török, D., Eszlári, N., Sütöri, S., Sperlágh, B., Anderson, I. M., Deakin, J. F. W., Bagdy, G., Juhász, G., et al. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25020865 · Cited by 7
“…a tt empts, current hopelessness, or thoughts of death appeared. and predictors of current suicidal risk, including hopelessness [44], death [45], and suicidal ideation [46] in a large European general population a clumping procedure to investigate all available variants along the gene. two…”
BackgroundMethodDeveloping a suicide risk model for use in the Indian Health Service
Adams, R., Haroz, E. E., Rebman, P., Suttle, R., Grosvenor, L., Bajaj, M. A., Dayal, R. R., Maggio, D., Kettering, C. L., & Goklish, N. A. (2024) · npj Mental Health Research · doi:10.1038/s44184-024-00088-5 · Cited by 7
“Simon et al. developed a logistic regression model for 90-day suicide attempts and death using health records from a cohort of patients containing only 1% AI/AN patients.”
MethodResultInfluentialPrediction of early‐onset bipolar using electronic health records
Wang, B., Sheu, Y.-H., Lee, H., Mealer, R. G., Castro, V. M., & Smoller, J. W. (2025) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.14131 · Cited by 9
Sheu, Y.-H., Simm, J., Wang, B., Lee, H., & Smoller, J. W. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01552-y · Cited by 7
AI-Y: An AI Checklist for Population Ethics Across the Global Context
Hswen, Y., Naslund, J. A., Hurley, M., Ragon, B., Handley, M. A., Fang, F., Haroz, E. E., Nabende, J. N., van Heerden, A. C., & Nsoesie, E. O. (2025) · Current Epidemiology Reports · doi:10.1007/s40471-025-00362-w · Cited by 8
Gunlicks‐Stoessel, M., Liu, Y., Parkhill, C., Morrell, N., Choy-Brown, M., Mehus, C. J., Hetler, J., & August, G. J. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02410-1 · Cited by 9
Depression, non-medical pain prescriptions, and suicidal behavior in cancer survivors
Mwobobia, J. M., White, M., Osazuwa‐Peters, O. L., Boakye, E. A., Abouelella, D. K., Barnes, J. M., Viet, C. T., Ramos, K., Corbett, C. M., & Osazuwa‐Peters, N. (2025) · Journal of Cancer Survivorship · doi:10.1007/s11764-024-01740-x · Cited by 10
“This is based on previous literature showing increased risk of suicidal outcomes among individuals with chronic health conditions [32, 33].”
Suicide by Opioid: Exploring the Intentionality of the Act
Pergolizzi, J., Breve, F., Magnusson, P., Nalamasu, R., LeQuang, J., & Varrassi, G. (2021) · Cureus · doi:10.7759/cureus.18084 · Cited by 6
“Tools to help stratify patient risk have been developed [43-45].”
BackgroundYarborough, B. J. H., & Stumbo, S. P. (2022) · Archives of Suicide Research · doi:10.1080/13811118.2022.2064255 · Cited by 6
“The group of researchers who developed the MHRN suicide risk models undertook an examination to determine whether two risk models that estimated suicide death following an outpatient visit performed as accurately across race and ethnicities as they did across the whole population.”
BackgroundMethodInfluentialContemporary Topics in Patient Safety - Volume 1
Stawicki, S. P., & Firstenberg, M. S. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.87616 · Cited by 8
Integrating biobehavioral information to predict mood disorder suicide risk
Jackson, N., & Jabbi, M. (2022) · Brain Behavior & Immunity - Health · doi:10.1016/j.bbih.2022.100495 · Cited by 7
“Levi-Belz, 2018), and logistic regression models have identified self-report metrics and clinically relevant health data to predict suicide with high accuracy (Simon et al., 2018).”
BackgroundSun, L., & Zhang, J. (2019) · Psychiatry Research · doi:10.1016/j.psychres.2019.02.024 · Cited by 7
Associations of Common Depression Treatment Metrics With Patient-centered Outcomes
Carlo, A. D., Basu, A., & Unützer, J. (2021) · Medical Care · doi:10.1097/mlr.0000000000001540 · Cited by 6
Naifeh, J. A., Edwards, E. R., Bentley, K. H., Gildea, S. M., Kennedy, C. J., King, A. J., Kleiman, E. M., Luedtke, A. R., Nassif, T. H., Nock, M. K., et al. (2025) · Nature Mental Health · doi:10.1038/s44220-024-00360-9 · Cited by 6
“Our best model based on these considerations had a test sample AU-ROC of approximately 0.80, which is near the high end of machine learning SA prediction model discrimination in other general medical populations 12,20,21 .”
InfluentialScreening and Risk Algorithms for Detecting Pediatric Suicide Risk in the Emergency Department
Aseltine, R. H., Sacco, S. J., Rogers, S. C., Wang, F., Schwartz, H., & Chen, K. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.33505 · Cited by 7
The Unmet Needs for Major Depressive Disorder
McIntyre, R. S., & Gill, H. (2020) · Springer eBooks · doi:10.1007/978-3-030-42637-8_3 · Cited by 6
Whiteside, U. S., Richards, J. A., & Simon, G. E. (2021) · JMIR Formative Research · doi:10.2196/21127 · Cited by 6
“More recently, PHQ scores in combination with other EMR data have been used to predict suicide attempts and suicide using empirically derived risk scores [13].”
ResultSelf-citationRisk Assessment on Suicide Death and Attempt among Chinese Rural Youths Aged 15–34 Years
Sun, L., Zhang, J., Lamis, D. A., & Wang, Y. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph182413362 · Cited by 7
Yarborough, B. J. H., Stumbo, S. P., Rosales, A. G., Ahmedani, B. K., Boggs, J. M., Daida, Y. G., Negriff, S. L., Rossom, R. C., Simon, G., & Perrin, N. (2022) · Journal of Affective Disorders Reports · doi:10.1016/j.jadr.2022.100346 · Cited by 5
Self-citationHuang, Y., Chen, D., Levin, A. M., Ahmedani, B. K., Frank, C. B., Li, M., Wang, Q., Gui, H., & Sham, P. C. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02124-w · Cited by 6
“People using opioids often have poorer health, and lower socioeconomic status [19 – 21], while patients with conditions are more likely to be exposed to opioids due to antidepressant effects [22].”
BackgroundSelf-citationSariaslan, A., Larsson, H., Hawton, K. E., Pitkänen, J., Lichtenstein, P., Martikainen, P., & Fazel, S. (2023) · BMJ Mental Health · doi:10.1136/bmjment-2023-300758 · Cited by 6
“The associations were reduced in individuals without psychiatric disorders when self-harm events of undetermined intent were excluded (aORs: 2.8–3.9 vs 6.3–7.6) but not in those diagnosed with any of the psychiatric disorders (online supplemental etable 13).”
BackgroundInfluentialSimon, G. E., Shortreed, S. M., Johnson, E., Yaseen, Z. S., Stone, M. B., Mosholder, A. D., Ahmedani, B. K., Coleman, K. J., Coley, R. Y., Penfold, R. B., et al. (2023) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.5734 · Cited by 5
“11,14,19 20 – 22 23 also include linked EHR data on small proportions of covered populations.”
BackgroundSelf-citationBoggs, J. M., Quintana, L. M., Beck, A. L., Clarke, C. L., Richardson, L. A., Conley, A., Buckingham, E. T., Richards, J. A., & Betz, M. E. (2024) · Prevention Science · doi:10.1007/s11121-024-01641-6 · Cited by 7
“The suicide risk model was validated across seven health systems and 2,960,929 patients with 24,133 self-harm behaviors and 1240 suicide deaths (Simon et al., 2018).”
InfluentialSelf-citationMortier, P., Amigo, F., Bhargav, M., Conde, S., Ferrer, M., Flygare, O., Kizilaslan, B., Moreno, L. L., Leis, Á., Mayer, M. Á., et al. (2024) · BMC Psychiatry · doi:10.1186/s12888-024-05659-6 · Cited by 12
“While statistical risk prediction models have shown some promise in improving suicide risk prediction accuracy [55–58] and cost-effectiveness [59], their application in clinical settings remains limited.”
BackgroundA Learning Health System to Generate and Accelerate Innovation: The HealthPartners Institute
Pronk, N. P., Augustson, J. L., Kottke, T. E., Dehmer, S. P., Margolis, K. L., & Frisch, K. (2024) · NEJM Catalyst · doi:10.1056/cat.23.0312 · Cited by 6
Suicide and Substance Use Disorder
Connery, H. S., Korte, F. M., & McHugh, R. K. (2020) · Psychiatric Annals · doi:10.3928/00485713-20200311-01 · Cited by 7
Britton, P. C., Crasta, D. J., Bohnert, K. M., Kane, C. P., Klein, J. M., & Pigeon, W. R. (2021) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2021.08.027 · Cited by 6
Predictors of Intentional Self -Harm Among Medicaid Mental Health Clinic Clients In New York
Rahman, M., Leckman‐Westin, E., Stanley, B., Kammer, J., Layman, D. M., Labouliere, C. D., Cummings, A., Vasan, P., Vega, K., Green, K. L., et al. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.11.035 · Cited by 6
Performance of a Prediction Model of Suicide Attempts Across Race and Ethnicity
Papini, S., Hsin, H., Kipnis, P., Liu, V. X., Lu, Y., Sterling, S., & Iturralde, E. (2023) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2022.5063 · Cited by 6
Predicting Outcomes of Antidepressant Treatment in Community Practice Settings
Simon, G. E., Cruz, M., Boggs, J. M., Beck, A. L., Shortreed, S. M., & Coley, R. Y. (2023) · Psychiatric Services · doi:10.1176/appi.ps.20230380 · Cited by 5
Self-citationAutomated detection and prediction of suicidal behavior from clinical notes using deep learning
Bunnell, B. E., Tsalatsanis, A., Chaphalkar, C., Robinson, S., Klein, S., Cool, S., Szwast, E. A., Heider, P. M., Wolf, B. J., & Obeid, J. S. (2025) · PLoS ONE · doi:10.1371/journal.pone.0331459 · Cited by 5
“Furthermore, we intend to compare the text-based predictors with traditional models using structured EHR data (e.g., history of previous mental health codes, medications, and other variables; [18]) as well as datasets with patients at high risk for ISH (e.g., with a history of depression).”
Behavior of prediction performance metrics with rare events
Minus, E., Coley, R. Y., Shortreed, S. M., & Williamson, B. D. (2025) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2025.112046 · Cited by 7
Self-citationThe rapid shift to virtual mental health care: Examining psychotherapy disruption by rurality status
Miller-Matero, L. R., Knowlton, G. S., Vagnini, K. M., Yeh, H.-H. H., Rossom, R. C., Penfold, R. B., Simon, G. E., Akinyemi, E., Abdole, L., Hooker, S. A., et al. (2023) · The Journal of Rural Health · doi:10.1111/jrh.12818 · Cited by 5
Self-citationSuicide typologies among Medicaid beneficiaries, North Carolina 2014–2017
Sivaraman, J. J. C., Ranapurwala, S. I., Proescholdbell, S., Naumann, R. B., Greene, S. B., & Marshall, S. W. (2022) · BMC Psychiatry · doi:10.1186/s12888-022-03741-5 · Cited by 5
Cruz, M., Shortreed, S. M., Richards, J. A., Coley, R. Y., Yarborough, B. J. H., Walker, R. L., Johnson, E., Ahmedani, B. K., Rossom, R. C., Coleman, K. J., et al. (2022) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.21m14178 · Cited by 5
Self-citationBerchuck, S. I., Jammal, A. A., Page, D. I., Somers, T. J., & Medeiros, F. A. (2022) · Translational Vision Science & Technology · doi:10.1167/tvst.11.10.6 · Cited by 5
“Emergency medicine 81,827 (23) 11,822 (49) 70,005 (21)”
Carlson, K. F., Gilbert, T. A., Maxim, L. A., Hooker, E. R., Shull, S., DeBeer, B. B., DeFrancesco, S., & Denneson, L. M. (2023) · Academic Emergency Medicine · doi:10.1111/acem.14711 · Cited by 6
How to select predictive models for decision-making or causal inference
Doutreligne, M., & Varoquaux, G. P. (2025) · GigaScience · doi:10.1093/gigascience/giaf016 · Cited by 10
Digital Health and Healthcare Quality: A Primer on the Evolving 4th Industrial Revolution
Otokiti, A. U. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.94054 · Cited by 5
Detecting and distinguishing indicators of risk for suicide using clinical records
Ahmedani, B., Cannella, C., Yeh, H., Westphal, J., Simon, G. E., Beck, A. L., Rossom, R., Lynch, F., Lu, C. Y., Owen-Smith, A., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02051-4 · Cited by 5
Self-citationMaß, R., Backhaus, K., Szelies, M., & Unkelbach, B. K. (2023) · Verhaltenstherapie · doi:10.1159/000528902 · Cited by 5
Progovac, A. M., Mullin, B. O., Yang, X., Kibugi, L., Mwizerwa, D., Hatfield, L. A., Schuster, M. A., McDowell, A., & Cook, B. L. (2023) · Transgender Health · doi:10.1089/trgh.2022.0146 · Cited by 6
Arias, S. A., Sperber, K. G., Jones, R. N., Taxman, F. S., Miller, T. R., Zylberfuden, S., Weinstock, L. M., Brown, G. K., Ahmedani, B. K., & Johnson, J. E. (2023) · BMC Health Services Research · doi:10.1186/s12913-023-10249-5 · Cited by 5
“…extracted from claims data using ICD-10 codes [29, 46–58]; an increase in the number of outpatient BH (i.e., mental health or substance use) visits extracted from claims data; fewer mental health inpatient hospitalizations and ED visits extracted from claims data using MHRN algorithms [29, 46–57,”
MethodInfluentialSelf-citationPediatric hospital screening for suicide risk in adolescents referred for maltreatment
Azzopardi, C., Greenblatt, A., Korczak, D. J., & Smith, T. D. (2020) · Children and Youth Services Review · doi:10.1016/j.childyouth.2020.105500 · Cited by 5
Fang, X., Yu, H., Wu, C., Tan, J., Sun, F., Qian, N., Zhou, H., Zhou, J., & Zhang, X. (2025) · BMC Medicine · doi:10.1186/s12916-025-04380-9 · Cited by 5
“A prospective cohort study conducted in the USA revealed that nearly half of individuals who died by suicide had a history of mental health diagnoses or had received related treatment [27].”
Emergency Department Use in Adolescent and Young Adult Cancer Early Survivors from 2006 to 2020
Wernli, K. J., Haupt, E. C., Chawla, N., Osuji, T. A., Shen, E., Smitherman, A. B., Casperson, M., Kirchhoff, A. C., Zebrack, B., Keegan, T. H. M., et al. (2024) · Journal of Adolescent and Young Adult Oncology · doi:10.1089/jayao.2023.0174 · Cited by 4
Liu, K., Jin, W., Ying, Z., Fan, J., Sheng, X., Tang, C., Zhou, D., Guo, J., Chen, G., & Bai, R. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.119420 · Cited by 4
Using predictive analytics to improve pragmatic trial design
Shortreed, S. M., & Simon, G. E. (2020) · Clinical Trials · doi:10.1177/1740774520910367 · Cited by 4
“We use the Mental Health Research Network risk prediction tool 16 and compare trials that identify eligible patients using patient-reported suicidal ideation (response to PHQ ninth item) and trials that use calculated suicide risk prediction scores, based on data from the patient’s electronic…”
MethodInfluentialSelf-citationZhang, Y., He, Y., Pang, Y. K., Su, Z., Wang, Y., Zhou, Y., Lu, Y., Jiang, Y., Han, X., Song, L., et al. (2024) · BMC Psychology · doi:10.1186/s40359-024-01607-x · Cited by 5
“…participant was experiencing SI, as previous studies have demonstrated the acceptable accuracy of this single item against specific suicide assessment tools, such as the Columbia Suicide Severity Rating Scale [25], and such a score on Item 9 of the PHQ-9 is associated with suicide attempts [26].”
MethodIdentifying opportunities for informatics-supported suicide prevention: the case of Caring Contacts.
Burkhardt, H. A., Laine, M., Kerbrat, A. H., Cohen, T., Comtois, K. A., & Hartzler, A. L. (2022) · PubMed Central · Cited by 4
Stephenson, M. E., Ohlsson, H., Lannoy, S., Sundquist, J., Sundquist, K., & Edwards, A. C. (2024) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.13675 · Cited by 4
Jayasinghe, L., Bittar, A., Dutta, R., & Stewart, R. J. (2020) · BMJ Open · doi:10.1136/bmjopen-2019-036186 · Cited by 4
Prediction of Suicide Risk Using Machine Learning and Big Data
Roza, T. H., Salgado, T. A., Machado, C. D. S., Watts, D., Bebber, J. C., de Freitas, T. R. O., Rabelo‐da‐Ponte, F. D., Kapczinski, F. P., & Passos, I. C. (2023) · Springer eBooks · doi:10.1007/978-3-031-10698-9_11 · Cited by 5
Dayal, R. R., Yang, P. L., Sisson, L. N., Bajaj, M. A., Archuleta, S., Yao, S., Park, D. H., Fujii-Rios, H., & Haroz, E. E. (2025) · Journal of Medical Internet Research · doi:10.2196/57602 · Cited by 5
“Prognostic risk scores can include short-term and long-term risk factors to improve predictability [43] and better guide treatment plans over the following weeks to prevent future suicide [44].”
Evaluating the role of cost and fairness in machine learning models of suicide risk
Kitchen, C., Belouali, A., Nestadt, P. S., Wilcox, H. C., & Kharrazi, H. H. K. (2026) · Scientific Reports · doi:10.1038/s41598-026-50095-z · Cited by 5
“LASSO is frequently used in machine learning workflows to reduce collinearity of inputs and select features 4,8 .”
Palomino, K., & Correa, C. R. B. (2023) · IEEE Intelligent Systems · doi:10.1109/mis.2023.3287409 · Cited by 5
Borowski, S. C., Caine, E. D., Kumar, S. A., Karras, E., Gamble, S. A., & Vogt, D. S. (2025) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2025.01.023 · Cited by 3
Stumbo, S. P., Hooker, S. A., Rossom, R. C., Miley, K. M., Ahmedani, B. K., Lockhart, E. A., Yeh, H.-H., & Yarborough, B. J. H. (2025) · BMC Psychiatry · doi:10.1186/s12888-025-06760-0 · Cited by 4
“In contrast, the MHRN suicide risk identification models include data from approximately 20 million visits made by three million patients at seven large integrated health care systems, with broad diversity in sex, age, race, ethnicity, income, education and insurance [17].”
InfluentialSelf-citationAyilara, O. F., White, A., Dunleavy, G., Ferro, M. A., Araya, R., Schmider, J., Seward, N., & Sajobi, T. T. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.120226 · Cited by 3
Yalçın, M. E., & Paltun, S. C. (2025) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1675959 · Cited by 3
McGarry, A. J., Auinger, P., Kieburtz, K. D., Bredlau, A., Hersch, S. M., & Rosas, H. D. (2022) · Neurology Clinical Practice · doi:10.1212/cpj.0000000000001161 · Cited by 3
Predicting Poor Outcomes Among Individuals Seeking Care for Major Depressive Disorder
Liberman, J. N., Pesa, J. A., Rui, P., Teeple, A., Lakey, S. L., Wiggins, E., & Ahmedani, B. K. (2022) · Psychiatric Research and Clinical Practice · doi:10.1176/appi.prcp.20220011 · Cited by 3
“Recently, the application of advanced analytics to available digital healthcare data has highlighted the precision of models that predict suicidality using structured data (18, 19) and unstructured data, applying natural language processing techniques (20).”
BackgroundSelf-citationNielssen, O. B., Staples, L., Karin, E., Ryan, K., Kayrouz, R., Dear, B. F., Cross, S. P. M., & Titov, N. (2023) · BJPsych Open · doi:10.1192/bjo.2023.60 · Cited by 3
P4 suicidality screener: Literature synthesis and results from two randomized trials
Bullington, C., & Kroenke, K. (2023) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2023.11.004 · Cited by 3
Ripperger, M. A., Kolli, J., Wilimitis, D., Robinson, K., Reale, C., Novak, L. L., Cunningham, C. A., Kasuske, L., Grover, S. G., Ribeiro, J. D., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.42750 · Cited by 4
Yeh, P. G., & Tsai, J. (2024) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2024.09.044 · Cited by 3
Using transfer learning to improve prediction of suicide risk in acute care hospitals
Sacco, S. J., Chen, K., Wang, F., Rogers, S. C., & Aseltine, R. H. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf126 · Cited by 4
Chapter 30. Suicide Prevention Programs
Yellowlees, P., & Liu, B. (2020) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615375288.lg30 · Cited by 3
Puig-Amores, I., Gordillo, I. C., & Parra, G. M.-M. (2023) · Healthcare · doi:10.3390/healthcare11101505 · Cited by 3
Estimating the Number of Self-Harm Events Not Identified by Encounter Diagnoses in Health RecordsPreprint
Simon, G. E., Shortreed, S. M., Boggs, J. M., Clarke, G. N., Rossom, R. C., Richards, J. A., & Beck, A. L. (2020) · medRxiv · doi:10.1101/2020.09.24.20200998 · Cited by 3
Self-citationRashkovich, S., Xi, W., Sholle, E., Sharma, M., Casale, K., Alvarez, M., Mhaimeed, N., Sombrotto, L. B., Pathak, J., & Shayevitz, C. (2025) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2025.07.013 · Cited by 2
Pagliaccio, D., Kirshenbaum, J. S., Greenblatt, J., Mroczkowski, M. M., Chernick, L. S., Dayan, P. S., & Auerbach, R. P. (2025) · JAACAP Open · doi:10.1016/j.jaacop.2025.08.002 · Cited by 3
“9 13 Large-scale EHR data across ages (ie, nonspecific to children) have been examined to predict suicide deaths within 90 days of ED or outpatient visits, 11,14 and often implicate sociodemographics, psychiatric diagnoses, and medication factors in predicting suicidal behavior.”
Kim, D. G., Kim, S. H., Kang, D. R., Nam, S. W., Lee, J. Y., & Lee, J. (2025) · Clinical Kidney Journal · doi:10.1093/ckj/sfaf370 · Cited by 2
Navigating extreme class imbalance in suicide risk prediction
Kitchen, C., Belouali, A., Nestadt, P. S., Wilcox, H. C., & Kharrazi, H. H. K. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1679618 · Cited by 3
Clinical practice guideline for assessment and management of depression in India
Tripathi, A., Shukla, R., Kar, S. K., Gupta, S., Saran, P., Pattojoshi, A., & Rao, T. (2026) · Indian Journal of Psychiatry · doi:10.4103/indianjpsychiatry_1321_25 · Cited by 4
“[23,24]”
Blocking the Reflection: Milestones and Hurdles for Digital Twins in Mental Health
Verhees, F. G., Wiest, I. C., Kather, J. N., Kambeitz, J., & Mikoláš, P. (2026) · Pharmacopsychiatry · doi:10.1055/a-2816-2869 · Cited by 3
Kitchen, C., Nestadt, P. S., Wilcox, H. C., Richards, T., Lasser, E. C., & Kharrazi, H. H. K. (2026) · JAMIA Open · doi:10.1093/jamiaopen/ooag062 · Cited by 3
Suicide Risk Models Are Ready—Our Health Systems Are Not
Xiao, Y., Walsh, C. G., Kharrazi, H. H. K., & Mann, J. J. (2026) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2026.2397 · Cited by 2
Eccles, H., Nadouri, D., Nannarone, M., Lashewicz, B., Schmitz, N., Patten, S. B., Manuel, D. G., & Wang, J. (2021) · BMC Psychiatry · doi:10.1186/s12888-021-03590-8 · Cited by 2
“in clinical samples [14, 15], and for suicide [16, 17].”
BackgroundAz öngyilkossági rizikó becslése a klinikai gyakorlatban.
Szabó, J., Vörös, V., Molnár, C., Fekete, S., Tényi, T., & Osváth, P. (2022) · Orvosi Hetilap · doi:10.1556/650.2022.32464 · Cited by 3
Cho, Y., Yook, V., Choi, Y.-H., Park, M. J., Kim, H.-S., & Jeon, H. J. (2022) · Precision and Future Medicine · doi:10.23838/pfm.2022.00100 · Cited by 2
A Reproducible Model Based on Clinical Text for Predicting Suicidal Behavior
Obeid, J. S., Tsalatsanis, A., Chaphalkar, C., Robinson, S., Klein, S., Cool, S., Szwast, E. A., & Bunnell, B. E. (2024) · Studies in health technology and informatics · doi:10.3233/shti231257 · Cited by 2
“Electronic health records (EHRs) have been shown to be useful in risk assessment [1].”
Risk and protective correlates of suicidality in the military health and well-being project
Schafer, K. M., Melia, R., & Joiner, T. E. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.07.141 · Cited by 2
Penfold, R. B., Yoo, H. I., Richards, J. A., Crossnohere, N. L., Johnson, E., Pabiniak, C. J., Renz, A. D., Campoamor, N. B., Simon, G. E., & Bridges, J. F. P. (2024) · JAMIA Open · doi:10.1093/jamiaopen/ooae113 · Cited by 2
Self-citationImportance of variables from different time frames for predicting self-harm using health system data
Wolock, C. J., Williamson, B. D., Shortreed, S. M., Simon, G. E., Coleman, K. J., Yeargans, R., Ahmedani, B. K., Daida, Y. G., Lynch, F. L., Rossom, R. C., et al. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104750 · Cited by 2
Self-citationSandbu, M., Javo, A. C., Thapa, S. B., Rø, K. I., Preljevic, V., & Tyssen, R. (2024) · Transcultural Psychiatry · doi:10.1177/13634615241296297 · Cited by 2
“Evaluating such risk is generally found to be one of the most challenging tasks in psychiatric work (Simon et al., 2018).”
Xi, W., Banerjee, S., Alexopoulos, G. S., Olfson, M., & Pathak, J. (2025) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2025.01.054 · Cited by 2
Rojo-Romero, M. A., Gutiérrez-Nájera, N., Cruz-Fuentes, C. S., Romero-Pimentel, A. L., Mendoza-Morales, R., García‐Dolores, F., Morales-Marín, M. E., Castro-Martínez, X. H., González-Sáenz, E. E., Torres-Campuzano, J. A., et al. (2025) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1429953 · Cited by 2
Mental pain and lifetime suicide attempts in early adolescence: a preliminary study
García‐Ormaza, J., Tabares, J. V., Ammendola, E., & Muela, A. (2025) · Child and Adolescent Psychiatry and Mental Health · doi:10.1186/s13034-025-00883-8 · Cited by 4
“However, evidence suggests that suicidal ideation alone is an unreliable marker for predicting suicidal behavior [20–22].”
Identifying patients at risk of suicide using data from health information exchanges
Sacco, S. J., Chen, K., Jin, J., Tang, B., Wang, F., & Aseltine, R. H. (2025) · BMC Public Health · doi:10.1186/s12889-025-22752-x · Cited by 2
“Third, our algorithm displayed low positive predictive values at 90% and 95% specificity (i.e., many patients labeled at risk were false positives), which is very similar to the performance of other suicide risk algorithms [8, 10, 13] and survey-based risk screening in adult populations [8, 35, 36].”
InfluentialGericke, F., Voorspoels, W., Peeters, E., Demyttenaere, K., Sabbe, M. B., Bantjes, J. R., & Bruffaerts, R. (2025) · Psychiatry Research · doi:10.1016/j.psychres.2025.116698 · Cited by 3
Are New Standards for Assessing and Managing Suicidal Patients Needed in Canada?
Links, P. S., Eynan, R., & Shah, R. (2019) · The Canadian Journal of Psychiatry · doi:10.1177/0706743718821341 · Cited by 2
Survival As a Patient-Centered Outcome of Mental Health Care
Schoenbaum, M. (2019) · Psychiatric Services · doi:10.1176/appi.ps.201900091 · Cited by 2
Metadata
- Authors
- Simon, G. E., Johnson, E., Lawrence, J. M., Rossom, R. C., Ahmedani, B., Lynch, F. L., Beck, A., Waitzfelder, B., Ziebell, R., Penfold, R. B., & Shortreed, S. M.
- Affiliations
- Kaiser Permanente Washington Health Research Institute (US)
- ORCID
- Venue
- American Journal of Psychiatry
- Published
- 2018-10-01
- Volume
- 175
- Issue
- 10
- Pages
- 951-960
- ISSN
- 0002-953X (ISSN-L)
- 1535-7228
- PMC
- PMC6167136
- OpenAlex
- W2804266670
- Cited by
- 400
- References
- 46
- Citations by year
- 2026: 42 · 2025: 57 · 2024: 37 · 2023: 56 · 2022: 53 · 2021: 49 · 2020: 55 · 2019: 44 · 2018: 7
- FWCI
- 45.53
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- American Journal of Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (U19MH092201)
- Topics
- Social Sciences › Psychology › Clinical Psychology › Suicide and Self-Harm Studies
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- suicide attempts, suicide mortality, suicide risk prediction, outpatient mental health visits, primary care visits, electronic health records, LASSO feature selection, risk score, substance use, psychiatric medication, mental health diagnoses, prediction model validation, death certificates, emergency department care, inpatient care, self-report questionnaire
- MeSH terms
- Adolescent, Adult, Aged, Female, Humans, Male, Middle Aged, Models, Psychological, Outpatients, Surveys and Questionnaires, Risk Factors, Suicide, Attempted, Young Adult, Electronic Health Records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
