Predicting suicidal behavior from longitudinal electronic health records.

Suicide and suicidal behavior

Nock, M. K., Borges, G., Bromet, E. J., Cha, C. B., Kessler, R. C., & Lee, S. (2008) · Epidemiologic Reviews · doi:10.1093/epirev/mxn002 · Cited by 2,897

Cross-national prevalence and risk factors for suicidal ideation, plans and attempts

Nock, M. K., Borges, G., Bromet, E. J., Alonso, J., Angermeyer, M., Beautrais, A., Bruffaerts, R., Chiu, W. T., de Girolamo, G., Gluzman, S., et al. (2008) · British Journal of Psychiatry · doi:10.1192/bjp.bp.107.040113 · Cited by 2,654

Clinical versus mechanical prediction: a meta-analysis

Grove, W. M., Zald, D. H., Lebow, B. S., Snitz, B. E., & Nelson, C. (2000) · Psychological Assessment · doi:10.1037/1040-3590.12.1.19 · Cited by 1,640

Machine learning for medical diagnosis: history, state of the art and perspective

Kononenko, I. (2001) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(01)00077-x · Cited by 1,306

Clinical correlates of inpatient suicide

Busch, K. A., Fawcett, J., & Jacobs, D. G. (2003) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.v64n0105 · Cited by 750

Measuring the Suicidal Mind: Implicit Cognition Predicts Suicidal Behavior

Nock, M. K., Park, J. M., Finn, C. T., Deliberto, T. L., Dour, H. J., & Banaji, M. R. (2010) · Psychological Science · doi:10.1177/0956797610364762 · Cited by 624

Predicting Suicides After Psychiatric Hospitalization in US Army Soldiers: The Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS)

Kessler, R. C., Warner, C. H., Ivany, C., Petukhova, M. V., Rose, S., Bromet, E. J., Brown, M., Cai, T., Colpe, L. J., Cox, K. L., et al. (2015) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2014.1754 · Cited by 447

Improving the Short-Term Prediction of Suicidal Behavior

Glenn, C. R., & Nock, M. K. (2014) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2014.06.004 · Cited by 328

Trends in US emergency department visits for attempted suicide and self-inflicted injury, 1993-2008.

Ting, S. A., Sullivan, A. F., Boudreaux, E. D., Miller, I., & Camargo, C. A. (2012) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2012.03.020 · Cited by 252

Prenatal antidepressant exposure is associated with risk for attention-deficit hyperactivity disorder but not autism spectrum disorder in a large health system

Clements, C. C., Castro, V. M., Blumenthal, S. R., Rosenfield, H. R., Murphy, S. N., Fava, M., Erb, J. L., Churchill, S. E., Kaimal, A. J., Doyle, A. E., et al. (2015) · Molecular Psychiatry · doi:10.1038/mp.2014.90 · Cited by 197

Clinical implementation of suicide risk prediction models in healthcare: a qualitative study

Yarborough, B. J. H., Stumbo, S. P., Schneider, J. L., Richards, J. A., Hooker, S. A., & Rossom, R. C. (2022) · BMC Psychiatry · doi:10.1186/s12888-022-04400-5 · Cited by 30

A machine learning model to predict the risk of depression in US adults with obstructive sleep apnea hypopnea syndrome: a cross-sectional study

Li, E., Ai, F., & Liang, C. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1348803 · Cited by 37

Applying machine learning on health record data from general practitioners to predict suicidality

van Mens, K., Elzinga, E., Nielen, M. M. J., Lokkerbol, J., Poortvliet, R., Donker, G., Heins, M. J., Korevaar, J. C., Dückers, M., Aussems, C., et al. (2020) · Internet Interventions · doi:10.1016/j.invent.2020.100337 · Cited by 31

Modes of Resting Functional Brain Organization Differentiate Suicidal Thoughts and Actions

Cáceda, R., Bush, K., James, G. A., Stowe, Z. N., & Kilts, C. (2018) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.17m11901 · Cited by 30

Predicting suicidal behavior and self-harm after general hospitalization of adults with serious mental illness

Edgcomb, J. B., Shaddox, T. R., Hellemann, G., & Brooks, J. O. (2020) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2020.10.024 · Cited by 28

Alcohol use disorder and non‐fatal suicide attempt: findings from a Swedish National Cohort Study

Edwards, A. C., Ohlsson, H., Mościcki, E. K., Crump, C., Sundquist, J., Kendler, K. S., & Sundquist, K. (2021) · Addiction · doi:10.1111/add.15621 · Cited by 34

Machine learning to advance the prediction, prevention and treatment of eating disorders

Wang, S. B. (2021) · European Eating Disorders Review · doi:10.1002/erv.2850 · Cited by 35

Predictors of self-harm emergency department visits in adolescents: A statewide longitudinal study

Goldman-Mellor, S. J., Kwan, K., Boyajian, J. G., Gruenewald, P. J., Brown, P., Wiebe, D. J., & Cerdá, M. (2018) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2018.12.004 · Cited by 29

The Brief Death Implicit Association Test: Scoring recommendations, reliability, validity, and comparisons with the Death Implicit Association Test.

Millner, A. J., Coppersmith, D. D. L., Teachman, B. A., & Nock, M. K. (2018) · Psychological Assessment · doi:10.1037/pas0000580 · Cited by 28

A Comparison of Logistic Regression Against Machine Learning Algorithms for Gastric Cancer Risk Prediction Within Real-World Clinical Data Streams

Huang, R. J., Kwon, N. S., Tomizawa, Y., Choi, A. Y., Hernandez‐Boussard, T., & Hwang, J. H. (2022) · JCO Clinical Cancer Informatics · doi:10.1200/cci.22.00039 · Cited by 28

Structured data vs. unstructured data in machine learning prediction models for suicidal behaviors: A systematic review and meta-analysis

Hopkins, D., Rickwood, D., Hallford, D. J., & Watsford, C. R. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.945006 · Cited by 29

Ethical and Practical Considerations in the Use of a Predictive Model to Trigger Suicide Prevention Interventions in Healthcare Settings

Tucker, R. P., Tackett, M. J., Glickman, D. R., & Reger, M. A. (2018) · Suicide and Life-Threatening Behavior · doi:10.1111/sltb.12431 · Cited by 28

Temporally informed random forests for suicide risk prediction

Bayramli, I., Castro, V. M., Barak‐Corren, Y., Madsen, E. M., Nock, M. K., Smoller, J. W., & Reis, B. Y. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab225 · Cited by 27

Predictive structured–unstructured interactions in EHR models: A case study of suicide prediction

Bayramli, I., Castro, V. M., Barak‐Corren, Y., Madsen, E. M., Nock, M. K., Smoller, J. W., & Reis, B. Y. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00558-0 · Cited by 27

Suicide prevention in medical settings: The case for universal screening

Horowitz, L. M., Roaten, K., Pao, M., & Bridge, J. A. (2018) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2018.11.009 · Cited by 27

Clinician Suicide Risk Assessment for Prediction of Suicide Attempt in a Large Health Care System

Bentley, K. H., Kennedy, C. J., Khadse, P. N., Brooks, J. R., Madsen, E. M., Flics, M. J., Lee, H., Smoller, J. W., & Burke, T. A. (2025) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2025.0325 · Cited by 27

Accuracy and transportability of machine learning models for adolescent suicide prediction with longitudinal clinical records

Zang, C., Hou, Y., Lyu, D., Jin, J., Sacco, S. J., Chen, K., Aseltine, R. H., & Wang, F. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-03034-3 · Cited by 25

Unsupervised deep learning of electrocardiograms enables scalable human disease profiling

Friedman, S. F., Khurshid, S. S., Venn, R. A., Wang, X., Diamant, N., Achille, P. D., Weng, L.-C. C., Choi, S. H., Reeder, C., Pirruccello, J. P., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-024-01418-9 · Cited by 29

Predicting Suicidal Ideation in College Students with Mental Health Screening Questionnaires

Shim, G., & Jeong, B. (2018) · Psychiatry Investigation · doi:10.30773/pi.2018.08.21.3 · Cited by 24

Imputation and characterization of uncoded self-harm in major mental illness using machine learning

Kumar, P., Nestsiarovich, A., Nelson, S. J., Kerner, B., Perkins, D. J., & Lambert, C. G. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz173 · Cited by 23

Deep graph neural network-based prediction of acute suicidal ideation in young adults

Choi, K., Kim, S., Kim, B.-H., Jeon, H., Kim, J.-H., Jang, J., & Jeong, B. (2021) · Scientific Reports · doi:10.1038/s41598-021-95102-7 · Cited by 22

An efficient landmark model for prediction of suicide attempts in multiple clinical settings

Sheu, Y.-H., Sun, J., Lee, H., Castro, V. M., Barak‐Corren, Y., Song, E., Madsen, E. M., Gordon, W. J., Kohane, I. S., Churchill, S. E., et al. (2023) · Psychiatry Research · doi:10.1016/j.psychres.2023.115175 · Cited by 23

Deep sequential models for suicidal ideation from multiple source data

Peis, I., Olmos, P. M., Vera-Varela, C., Barrigon, M. L., Courtet, P., Baca-Garcia, E., & Artes-Rodriguez, A. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2919270 · Cited by 22

Identifying risk factors for mortality among patients previously hospitalized for a suicide attempt

Doshi, R., Chen, K., Wang, F., Schwartz, H., Herzog, A., & Aseltine, R. H. (2020) · Scientific Reports · doi:10.1038/s41598-020-71320-3 · Cited by 24

Using natural language processing to extract self-harm and suicidality data from a clinical sample of patients with eating disorders: a retrospective cohort study

Cliffe, C., Seyedsalehi, A., Vardavoulia, K., Bittar, A., Velupillai, S. U., Shetty, H., Schmidt, U. H., & Dutta, R. (2021) · BMJ Open · doi:10.1136/bmjopen-2021-053808 · Cited by 22

Predicting post-discharge self-harm incidents using disease comorbidity networks: A retrospective machine learning study

Xu, Z., Zhang, Q., & Yip, P. S. F. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.08.044 · Cited by 20

A Comorbidity Knowledge-Aware Model for Disease Prognostic Prediction

Xu, Z., Zhang, J., Zhang, Q., Xuan, Q., & YIP, P. S. F. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3070227 · Cited by 21

Shared genetic and environmental etiology between substance use disorders and suicidal behavior

Edwards, A. C., Ohlsson, H., Lannoy, S., Stephenson, M. E., Crump, C., Sundquist, J., Sundquist, K., & Kendler, K. S. (2021) · Psychological Medicine · doi:10.1017/s0033291721004256 · Cited by 22

Prediction models of suicide and non‐fatal suicide attempt after discharge from a psychiatric inpatient stay: A machine learning approach on nationwide Danish registers

Nielsen, S. D., Christensen, R. H. B., Madsen, T., Karstoft, K., Clemmensen, L. K. H., & Benros, M. E. (2023) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.13629 · Cited by 21

Prediction models for self-harm and suicide: a systematic review and critical appraisal

Seyedsalehi, A., Bailey, J. P. M., Ogonah, M. G. T., Fanshawe, T. R., & Fazel, S. (2025) · BMC Medicine · doi:10.1186/s12916-025-04367-6 · Cited by 25

Identifying risk factors for suicidal ideation across a large community healthcare system

Schriver, E., Lieblich, S., AlRabiah, R., Mowery, D. L., & Brown, L. A. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.07.047 · Cited by 22

Elevated Rates of Violence Victimization and Suicide Attempt Among Transgender and Gender Diverse Patients in an Urban, Safety Net Health System

Progovac, A. M., Tran, N. M., Mullin, B. O., Maia, J. L., Creedon, T. B., Dunham, E., Reisner, S. L., McDowell, A., Bird, N., Román, M. J. S., et al. (2021) · World Medical & Health Policy · doi:10.1002/wmh3.403 · Cited by 22

Integration and Validation of a Natural Language Processing Machine Learning Suicide Risk Prediction Model Based on Open-Ended Interview Language in the Emergency Department

Cohen, J., Wright-Berryman, J. L., Rohlfs, L., Trocinski, D. R., Daniel, L., & Klatt, T. W. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.818705 · Cited by 25

High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning

Dhaubhadel, S., Ganguly, K., Ribeiro, R. M., Cohn, J. D., Hyman, J. M., Hengartner, N., Kolade, B., Singley, A., Tanmoy Bhattacharya, A., Finley, P. D., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-51762-9 · Cited by 20

Technological Advances and the Future of Suicide Prevention: Ethical, Legal, and Empirical Challenges

Berman, A. L., & Carter, G. L. (2019) · Suicide and Life-Threatening Behavior · doi:10.1111/sltb.12610 · Cited by 19

Improving suicide risk prediction via targeted data fusion: proof of concept using medical claims data

Xu, W., Su, C., Li, Y., Rogers, S. C., Wang, F., Chen, K., & Aseltine, R. H. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab209 · Cited by 19

Emergency department visits and boarding for pediatric patients with suicidality before and during the COVID-19 pandemic

Zipursky, A. R., Olson, K. L., Bode, L., Geva, A., Jones, J. R., Mandl, K. D., & McMurry, A. (2023) · PLoS ONE · doi:10.1371/journal.pone.0286035 · Cited by 20

Potential use of text classification tools as signatures of suicidal behavior: A proof-of-concept study using Virginia Woolf’s personal writings

Berni, G. D. Á., Rabelo‐da‐Ponte, F. D., Librenza‐Garcia, D., Boeira, M. V., Kauer‐Sant’Anna, M., Passos, I. C., & Kapczinski, F. P. (2018) · PLoS ONE · doi:10.1371/journal.pone.0204820 · Cited by 19

Frequency of rehospitalization after hospitalization for suicidal ideation or suicidal behavior in patients with depression

Cepeda, M. S., Schuemie, M. J., Kern, D. M., Reps, J., & Canuso, C. M. (2020) · Psychiatry Research · doi:10.1016/j.psychres.2020.112810 · Cited by 19

Disparities in Suicidality by Gender Identity Among Medicare Beneficiaries

Progovac, A. M., Mullin, B. O., Dunham, E., Reisner, S. L., McDowell, A., Román, M. J. S., Dunn, M., Telingator, C. J., Lu, F. Q., Breslow, A. S., et al. (2020) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2020.01.004 · Cited by 19

Treatment patterns of patients diagnosed with major depressive disorder and suicidal ideation or attempt: a U.S. population-based study utilizing real-world data

Kern, D. M., Cepeda, M. S., & Wiegand, F. (2021) · BMC Psychiatry · doi:10.1186/s12888-021-03616-1 · Cited by 20

Deep Learning-based detection of psychiatric attributes from German mental health records

Madan, S., Zimmer, F. J., Balabin, H., Schaaf, S., Fröhlich, H., Fluck, J., Neuner, I., Mathiak, K., Hofmann‐Apitius, M., & Sarkheil, P. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104724 · Cited by 18

Trauma Matters: Integrating Genetic and Environmental Components of PTSD

Marchese, S., & Huckins, L. M. (2022) · Advanced Genetics · doi:10.1002/ggn2.202200017 · Cited by 21

Clinical Decision Support System: A Pragmatic Tool to Improve Acute Exacerbation of COPD Discharge Recommendations

Epstein, D., Barak‐Corren, Y., Isenberg, Y., & Berger, G. (2019) · COPD Journal of Chronic Obstructive Pulmonary Disease · doi:10.1080/15412555.2019.1593342 · Cited by 18

Managing risk and self-harm: Keeping young people safe

Westers, N. J., & Plener, P. L. (2020) · Clinical Child Psychology and Psychiatry · doi:10.1177/1359104519895064 · Cited by 16

Prediction of Suicidal Behaviors in the Middle-aged Population: Machine Learning Analyses of UK Biobank

Wang, J., Qiu, J., Zhu, T., Zeng, Y., Yang, H., Shang, Y., Yin, J. Y., Sun, Y., Qu, Y., Valdimarsdóttir, U. A., et al. (2023) · JMIR Public Health and Surveillance · doi:10.2196/43419 · Cited by 16

The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review

Somé, N. H., Noormohammadpour, P., & Lange, S. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1291362 · Cited by 20

Evaluation of Electronic Health Record-Based Suicide Risk Prediction Models on Contemporary Data

Walker, R. L., Shortreed, S. M., Ziebell, R. A., Johnson, E., Boggs, J. M., Lynch, F. L., Daida, Y. G., Ahmedani, B. K., Rossom, R. C., Coleman, K. J., et al. (2021) · Applied Clinical Informatics · doi:10.1055/s-0041-1733908 · Cited by 16

Suicide Evaluation in the Pediatric Emergency Setting

Ambrose, A. J. H., & Prager, L. M. (2018) · Child and Adolescent Psychiatric Clinics of North America · doi:10.1016/j.chc.2018.03.003 · Cited by 16

Intentional Self-Harm Among US Veterans With Traumatic Brain Injury or Posttraumatic Stress Disorder: Retrospective Cohort Study From 2008 to 2017

Rawat, B. P. S., Reisman, J., Pogoda, T. K., Liu, W., Rongali, S., Aseltine Jr, R. H., Chen, K., Tsai, J., Berlowitz, D., Yu, H., et al. (2023) · JMIR Public Health and Surveillance · doi:10.2196/42803 · Cited by 15

Exploring the Potential of Artificial Intelligence in Adolescent Suicide Prevention: Current Applications, Challenges, and Future Directions

Li, X., Chen, F., & Ma, L. (2024) · Psychiatry · doi:10.1080/00332747.2023.2291945 · Cited by 21

Most Individuals Are Seen in Outpatient Medical Settings Prior to Intentional Self-Harm and Suicide Attempts Treated in a Hospital Setting

Kammer, J., Rahman, M., Finnerty, M. T., Layman, D. M., Vega, K., Galfalvy, H., Labouliere, C. D., Brown, G. K., Green, K., Cummings, A., et al. (2020) · The Journal of Behavioral Health Services & Research · doi:10.1007/s11414-020-09717-1 · Cited by 19

Resampling to address inequities in predictive modeling of suicide deaths

Reeves, M., Bhat, H. S., & Goldman-Mellor, S. J. (2022) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2021-100456 · Cited by 17

Genome-wide association study of suicide death and polygenic prediction of clinical antecedentsPreprint

Docherty, A. R., Shabalin, A. A., DiBlasi, E., Monson, E. T., Mullins, N., Adkins, D. E., Bacanu, S., Bakian, A. V., Crowell, S. E., Darlington, T. M., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/234674 · Cited by 15

Digital revolution in depression: A technologies update for clinicians

Lazar, M. A., Pan, Z., Ragguett, R.-M., Lee, Y., Subramaniapillai, M., Mansur, R. B., Rodrigues, N., & McIntyre, R. S. (2017) · Personalized Medicine in Psychiatry · doi:10.1016/j.pmip.2017.09.001 · Cited by 14

Patient expectations of and experiences with a suicide risk identification algorithm in clinical practice

Yarborough, B. J. H., Stumbo, S. P., Schneider, J. L., Richards, J. A., Hooker, S. A., & Rossom, R. C. (2022) · BMC Psychiatry · doi:10.1186/s12888-022-04129-1 · Cited by 14

Validation of a Multivariable Model to Predict Suicide Attempt in a Mental Health Intake Sample

Papini, S., Hsin, H., Kipnis, P., Liu, V. X., Lu, Y., Girard, K., Sterling, S., & Iturralde, E. (2024) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2024.0189 · Cited by 14

Emerging trends in suicide prevention research

Oexle, N., Niederkrotenthaler, T., & DeLeo, D. (2019) · Current Opinion in Psychiatry · doi:10.1097/yco.0000000000000507 · Cited by 15

A Supervised Learning Approach for ICU Mortality Prediction Based on Unstructured Electrocardiogram Text Reports

Krishnan, G. S., & Kamath, S. S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-91947-8_13 · Cited by 13

Suicidality risk among adults with binge‐eating disorder

Bulik, C. M., Bertoia, M. L., Lu, M. J., Seeger, J. D., & Spalding, W. M. (2021) · Suicide and Life-Threatening Behavior · doi:10.1111/sltb.12768 · Cited by 13

Management of Depression and Anxiety in Perioperative Medicine

Rolfzen, M. L., Nägele, P., Conway, C. R., Gibbons, R. D., & Bartels, K. (2024) · Anesthesiology · doi:10.1097/aln.0000000000005076 · Cited by 13

Accuracy of ICD-10 Codes for Suicidal Ideation and Action in Pediatric Emergency Department Encounters

Xu, R., Bode, L., Geva, A., Mandl, K. D., & McMurry, A. (2024) · Pediatric Emergency Care · doi:10.1097/pec.0000000000003328 · Cited by 12

Text Analysis of Electronic Medical Records to Predict Seclusion in Psychiatric Wards: Proof of Concept

Hazewinkel, M. C., de Winter, R. F. P., van Est, R., Hyfte, D. V., Wijnschenk, D., Miedema, N., & Hoencamp, E. (2019) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2019.00188 · Cited by 12

Improving risk prediction for target subpopulations: Predicting suicidal behaviors among multiple sclerosis patients

Barak‐Corren, Y., Castro, V. M., Javitt, S., Nock, M. K., Smoller, J. W., & Reis, B. Y. (2023) · PLoS ONE · doi:10.1371/journal.pone.0277483 · Cited by 13

Geospatial identification of high youth suicide risk areas via electronic health records: Avenues for research and prevention efforts

Hill, R. M., Gushanas, K. L., Alvis, L. M., Dodd, C. G., Kurian, S. R., Saxena, J. E., Saxena, K., Kaplow, J. B., Rufino, K. A., & Williams, L. L. (2021) · Suicide and Life-Threatening Behavior · doi:10.1111/sltb.12701 · Cited by 11

Characterizing Opioid Overdoses Using Emergency Medical Services Data

Sivaraman, J. J. C., Proescholdbell, S., Ezzell, D., & Shanahan, M. E. (2021) · Public Health Reports · doi:10.1177/00333549211026802 · Cited by 11

Risk Model–Guided Clinical Decision Support for Suicide Screening

Walsh, C. G., Ripperger, M. A., Novak, L. L., Reale, C., Anders, S. H., Spann, A., Kolli, J., Robinson, K., Chen, Q., Isaacs, D. A., et al. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.52371 · Cited by 13

Differences in Health Care Utilization in the Year Before Suicide Death: A Population-Based Case-Control Study

Chock, M. M., Lin, J. C., Athyal, V. P., & Bostwick, J. M. (2019) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2019.04.037 · Cited by 10

Predictors of nonfatal suicide attempts within 30 days of discharge from psychiatric hospitalization: Sex-specific models developed using population-based registries

Zelkowitz, R. L., Jiang, T., Horváth–Puhó, E., Street, A. E., Lash, T. L., Sørensen, H. T., Rosellini, A. J., & Gradus, J. L. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.03.034 · Cited by 11

INPHOVIS: Interactive visual analytics for smartphone-based digital phenotyping

Mansoor, H., Gerych, W., Alajaji, A. S., Buquicchio, L., Chandrasekaran, K., Agu, E., Rundensteiner, E. A., & Rodriguez, A. C. I. (2023) · Visual Informatics · doi:10.1016/j.visinf.2023.01.002 · Cited by 8

Predictive value of suicidal risk assessment using data from China's largest suicide prevention hotline

Tong, Y., Yin, Y., Conner, K. R., Zhao, L., Wang, Y., Wang, X., & Conwell, Y. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.02.095 · Cited by 10

Grappling with the Future Use of Big Data for Translational Medicine and Clinical Care

Murphy, S. N., Castro, V. M., & Mandl, K. D. (2017) · Yearbook of Medical Informatics · doi:10.15265/iy-2017-020 · Cited by 12

Current Practices in Electronic Capture of Patient-Reported Outcomes for Measurement-Based Care and the Use of Patient Portals to Support Behavioral Health

Turvey, C., Lindsay, J. A., Chasco, E. E., Klein, D. M., Fuhrmeister, L. A., & Dindo, L. N. (2019) · Psychiatric Clinics of North America · doi:10.1016/j.psc.2019.08.006 · Cited by 10

User Feedback on the Use of a Natural Language Processing Application to Screen for Suicide Risk in the Emergency Department

Pease, J. L., Thompson, D., Wright-Berryman, J. L., & Campbell, M. (2023) · The Journal of Behavioral Health Services & Research · doi:10.1007/s11414-023-09831-w · Cited by 10

Evaluating the quality of word representation models for unstructured clinical Text based ICU mortality prediction

Krishnan, G. S., & Kamath, S. S. (2019) · International Conference on Distributed Computing and Networking (ICDCN) · doi:10.1145/3288599.3297118 · Cited by 10

Determining Distinct Suicide Attempts From Recurrent Electronic Health Record Codes: Classification Study

Bentley, K. H., Madsen, E., Song, E., Zhou, Y., Castro, V. M., Lee, H., Lee, Y. A., & Smoller, J. W. (2023) · JMIR Formative Research · doi:10.2196/46364 · Cited by 11

Contrasting rule and machine learning based digital self triage systems in the USA

Naved, B. A., & Luo, Y. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01367-3 · Cited by 9

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Tran, T., Luo, W., Phung, D., Harvey, R., Berk, M., Kennedy, R. L., & Venkatesh, S. (2014) · BMC Psychiatry · doi:10.1186/1471-244x-14-76 · Cited by 150

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