Developing a practical suicide risk prediction model for targeting high‐risk patients in the Veterans health Administration

Cited by (209)

Items 101–200 of 209

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2. Association Between Patterns of Alcohol Use and Short-Term Risk of Suicide Attempt Among Patients With and Without Reported Suicidal Ideation
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3. User-Centered Design of a Machine Learning Intervention for Suicide Risk Prediction in a Military Setting.
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5. Development of a 3-Step theory of suicide ontology to facilitate 3ST factor extraction from clinical progress notes
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7. Using natural language processing to evaluate temporal patterns in suicide risk variation among high-risk Veterans
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8. Using Natural Language Processing to develop risk-tier specific suicide prediction models for Veterans Affairs patients
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9. Stability of Suicide Risk Prediction Models During Changes in Health Care Delivery
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13. Suicide Mortality Among Veterans Health Administration Care Recipients With Suicide Risk Record Flags
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14. Multi-modal risk factors differentiate suicide attempters from ideators in military veterans with major depressive disorder
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15. Frequency and correlates of suicidal ideation and behaviors in treatment-seeking Post-9/11 Veterans
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17. Comparing the Army's Suicide Rate to the General U.S. Population: Identifying Suitable Characteristics, Data Sources, and Analytic Approaches
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19. A clinician’s guide to probabilistic suicide risk prediction tools: cautions and pitfalls
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23. The REACH VET Program and Mortality Outcomes Among Veterans at High Risk of Suicide
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24. Automated detection and prediction of suicidal behavior from clinical notes using deep learning
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25. Preprocessing of natural language process variables using a data-driven method improves the association with suicide risk in a large veterans affairs population
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26. Suicide typologies among Medicaid beneficiaries, North Carolina 2014–2017
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27. Automatically extracting social determinants of health for suicide: a narrative literature review
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28. Rates and Correlates of Suicidality in VA Intensive Case Management Programs
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29. National cross-sectional cohort study of the relationship between quality of mental healthcare and death by suicide
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30. Demographic Perspectives on Predicting Individual-level Mortality
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31. Impact of Implementation Facilitation on the REACH VET Clinical Program for Veterans at Risk for Suicide
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32. Machine learning applications related to suicide in military and Veterans: A scoping literature review
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36. Comparison of National and Local Approaches to Detecting Suicides in Healthcare Settings
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37. Local implementation evaluation of a suicide prevention predictive model at a large VA health care system
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38. Machine learning in the social and health sciences
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39. Derivation and validation of risk prediction for posttraumatic stress symptoms following trauma exposure
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40. Study protocol for a stepped-wedge, randomized controlled trial to evaluate implementation of a suicide risk identification model among behavioral health patients in three large health systems
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41. Retraining the veterans health administration’s REACH VET suicide risk prediction model for patients involved in the legal system
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42. External Validation and Updating of a Statistical Civilian-Based Suicide Risk Model in US Naval Primary Care
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43. Predicting Suicidal Behavior Among Indian Adults Using Childhood Trauma, Mental Health Questionnaires and Machine Learning Cascade Ensembles
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44. Design of a multicenter randomized controlled trial of a post‐discharge suicide prevention intervention for high‐risk psychiatric inpatients: The Veterans Coordinated Community Care Study
    Lauren M. Weinstock, Todd M. Bishop, Mark S. Bauer, Jeffrey Benware, Robert M. Bossarte, John C. Bradley, Steven K. Dobscha, Jessica Gibbs, Sarah M. Gildea, Hannah R. Graves et al. · 2024 · International Journal of Methods in Psychiatric Research
45. Differences in suicide acceptability by farming-related occupation, demographic, and religiosity factors, general social survey 2000–2022
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46. Testing Novel Payment and Delivery Approaches Through the Veterans Health Administration's New Center for Innovation
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47. Applying KT Network Complexity to a Highly-Partnered Knowledge Transfer Effort Comment on "Using Complexity and Network Concepts to Inform Healthcare Knowledge Translation"
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49. Improving Suicide Risk Detection and Clinical Follow-up after Discharge from Nursing Homes
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50. Functional and effective connectivity in major depressive disorder patients with suicidal thoughts: A resting-state fMRI study
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51. Comparing risk prediction models aimed at predicting hospitalizations for adverse drug events in community dwelling older adults: a protocol paper
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52. Computing 3‐Step Theory of Suicide Factor Scores From Veterans Health Administration Clinical Progress Notes
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53. Evaluating Evidence‐Based Psychotherapy Utilization Patterns Among Suicide‐Risk‐Stratified Veterans Diagnosed With Posttraumatic Stress Disorder
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54. Evaluating the accuracy of the Veterans Health Administration’s REACH VET suicide prediction model for legal involved veterans
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55. Developing a Suicide Risk Prediction Algorithm Using Electronic Health Record Data in Mental Health Care: Real-World Case Study
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56. Navigating extreme class imbalance in suicide risk prediction
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57. Predictive Properties of the C-SSRS Suicide Screen Among Individuals in Veterans Health Administration Care
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58. Development and Internal Validation of Interpretable Machine Learning Models for Identifying Burnout Syndrome Among Intensive Care Unit Nurses
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59. Risk factors for suicide one year after discharge from hospitalization for physical illness in Denmark
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60. Utilizing digital predictive biomarkers to identify Veteran suicide risk
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61. Association between cognitive functioning and lifetime suicidal ideation among Chinese older adults: the mediating effect of depression
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62. Risk and protective correlates of suicidality in the military health and well-being project
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63. Geography and risk of suicidal ideation and attempts post outpatient psychiatric visit in commercially insured US adults
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64. Describing county-level clustering patterns of suicide among Veterans Health Administration patients and all American adults, 2018–2019, using exploratory spatial data analysis
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65. Why the Nails Should Boss the Hammers
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66. Fujitsu HIKARI, a Healthcare Decision Support System based on Biomedical Knowledge
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67. Development and validation of short-term, medium-term, and long-term suicide attempt prediction models based on a prospective cohort in Korea
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68. A machine-learning-derived online screening tool for depressive symptoms in patients with cardiovascular and cerebrovascular diseases (CCVD): a cross-sectional study with temporal validation from CHARLS
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69. SAFEGUARD: Transforming Military Suicide Prevention Through Predictive Analytics and Targeted Interventions
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70. Life events extraction from healthcare notes for veteran acute suicide risk prediction
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71. Using natural language processing to assess MDD psychotherapy patterns among Veterans Affairs patients with suicide risk
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72. Artificial intelligence and suicide prevention: Promise and pitfalls
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73. Evaluating Text Analytic Frameworks for Mental Health Surveillance
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74. Suicide among Veterans in Veterans Health Administration care: Differences in methods by tier of predicted suicide risk
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75. Clinical AND Statistical Prediction: Integrating Clinician Judgment and Statistical Models to Improve the Prediction of Suicidal Behavior
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76. Implementing Predictive Models Within an Electronic Health Record System: Lessons from an External Validation of a Suicide Risk Model
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77. Moral Injury and Suicide Attempts in Nigerian Military Veterans
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78. Machine learning-based prediction of cognitive trajectories among middle-aged and elderly Chinese adults
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79. A New Measure of Psychiatric Severity Using Administrative Data: the Manifestations of Psychiatric Severity Index (MoPSI)
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80. Life Events Extraction From Healthcare Notes for Veteran Acute Suicide Prediction
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81. Challenges Associated with the Use of Policy to Identify and Manage Risk for Suicide and Interpersonal Violence Among Veterans and Other Americans
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82. Individual level prediction of emerging suicide events in the pharmacologic treatment of bipolar disorder
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83. PREDICTING SELF-HARM AND IDENTIFYING CAUSAL RISK FACTORS AMONG ADOLESCENTS WHO HAVE HAD CONTACT WITH U.S. CHILD PROTECTIVE SERVICES
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84. Fujitsu HIKARI, a Healthcare Decision Support System based on Biomedical Knowledge
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85. Development and Implementation of an Early Warning System for High Risk Patients for Suicide Attempt Using Deep Learning and Electronic Health Records (Preprint)
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87. Bridging Technical and Clinical needs: A Case Study on Developing a Suicide Risk Prediction Algorithm Using EHR-data in Real-World Mental Health Care (Preprint)
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89. Comparison of factors distinguishing suicide attempters among suicidal ideators and factors associated with suicidal ideation and attempts using machine learning models in South Korean adolescents
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90. AI-based algorithms trained with real-world data for suicide risk prevention in adult mental health patients: The IDICIUS Project. (Preprint)
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92. Additional file 1 of Clinical implementation of suicide risk prediction models in healthcare: a qualitative study
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93. Additional file 1 of Patient expectations of and experiences with a suicide risk identification algorithm in clinical practice
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94. Additional file 1 of Suicide typologies among Medicaid beneficiaries, North Carolina 2014–2017
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96. Predictive Accuracy of Natural Language Processing Extracted 3-Step Theory of Suicide Factor Scores Derived From Veterans’ Clinical Progress Notes
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98. Precision stratification of risk for suicidal behavior in people with bipolar depression
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99. Artificial intelligence in suicide risk assessment: a systematic literature review
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100. Evaluating Evidence-Based Psychotherapy Utilization Patterns Among Suicide-Risk-Stratified Veterans Diagnosed with Major Depressive Disorder
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