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Public Health Genomics and Precision Health Knowledge Base (v9.0)
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Precision Health Database|Search|Public Health Genomics and Precision Health Knowledge Base (PHGKB)
Last data update: Apr 23, 2024
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Predicting state level suicide fatalities in the united states with realtime data and machine learning.
Devashru Patel et al. Npj Ment Health Res 2024 3(1) 3
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Artificial Intelligence and the National Violent Death Reporting System: A Rapid Review.
Lisa C Lindley et al. Comput Inform Nurs 2024
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Validation of a Multivariable Model to Predict Suicide Attempt in a Mental Health Intake Sample.
Santiago Papini et al. JAMA Psychiatry 2024
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Analysis and evaluation of explainable artificial intelligence on suicide risk assessment.
Hao Tang et al. Sci Rep 2024 14(1) 6163
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Machine learning and the prediction of suicide in psychiatric populations: a systematic review.
Alessandro Pigoni et al. Transl Psychiatry 2024 14(1) 140
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Major Depressive Disorder Prediction Based on Sleep-Wake Disorders Symptoms in US Adolescents: A Machine Learning Approach from National Sleep Research Resource.
Jingsong Luo et al. Psychol Res Behav Manag 2024 17691-703
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Monitoring Mental Health: Legal and Ethical Considerations of Using Artificial Intelligence in Psychiatric Wards.
Barry Solaiman et al. Am J Law Med 2024 49(2-3) 250-266
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Predicting suicide death after emergency department visits with mental health or self-harm diagnoses.
Gregory E Simon et al. Gen Hosp Psychiatry 2024 8713-19
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Prediction models of suicide and non-fatal suicide attempt after discharge from a psychiatric inpatient stay: A machine learning approach on nationwide Danish registers.
Sara Dorthea Nielsen et al. Acta Psychiatr Scand 2023 148(6) 525-537
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Predicting suicide attempts among Norwegian adolescents without using suicide-related items: a machine learning approach.
E F Haghish et al. Front Psychiatry 2023 141216791
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Ecological Momentary Assessments and Passive Sensing in the Prediction of Short-Term Suicidal Ideation in Young Adults.
Ewa K Czyz et al. JAMA Netw Open 2023 6(8) e2328005
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Assessing Detection of Children With Suicide-Related Emergencies: Evaluation and Development of Computable Phenotyping Approaches.
Juliet Beni Edgcomb et al. JMIR Ment Health 2023 10e47084
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Suicide risk classification with machine learning techniques in a large Brazilian community sample.
Thiago Henrique Roza et al. Psychiatry Res 2023 325115258
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Public Surveillance of Social Media for Suicide Using Advanced Deep Learning Models in Japan: Time Series Study From 2012 to 2022.
Siqin Wang et al. J Med Internet Res 2023 25e47225
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Prediction of Diagnosis and Treatment Response in Adolescents With Depression by Using a Smartphone App and Deep Learning Approaches: Usability Study.
Jae Sung Kim et al. JMIR Form Res 2023 7e45991
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Research utility and limitations of textual data in the National Violent Death Reporting System: a scoping review and recommendations.
Linh N Dang et al. Inj Epidemiol 2023 10(1) 23
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Interpretable Estimation of Suicide Risk and Severity from Complete Blood Count Parameters with Explainable Artificial Intelligence Methods.
Neslihan Cansel et al. Psychiatr Danub 2023 35(1) 62-72
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A machine learning approach to identifying suicide risk among text-based crisis counseling encounters.
Meghan Broadbent et al. Front Psychiatry 2023 141110527
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From benchmark to bedside: transfer learning from social media to patient-provider text messages for suicide risk prediction.
Hannah A Burkhardt et al. J Am Med Inform Assoc 2023
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An efficient landmark model for prediction of suicide attempts in multiple clinical settings.
Yi-Han Sheu et al. Psychiatry research 2023 323115175
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Acoustic Analysis of Speech for Screening for Suicide Risk: Machine Learning Classifiers for Between- and Within-Person Evaluation of Suicidality.
Sooyeon Min et al. Journal of medical Internet research 2023 25e45456
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Complex modeling with detailed temporal predictors does not improve health records-based suicide risk prediction.
Susan M Shortreed et al. NPJ digital medicine 2023 6(1) 47
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Characterizing Female Firearm Suicide Circumstances: A Natural Language Processing and Machine Learning Approach.
Evan V Goldstein et al. American journal of preventive medicine 2023
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Associations Between Natural Language Processing-Enriched Social Determinants of Health and Suicide Death Among US Veterans.
Avijit Mitra et al. JAMA network open 2023 6(3) e233079
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A multicomponent digital intervention to promote help-seeking for mental health problems and suicide in sexual and gender diverse young adults: A randomized controlled trial.
Meng Han et al. PLoS medicine 2023 20(3) e1004197
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Prediction of suicidality in bipolar disorder using variability of intrinsic brain activity and machine learning.
Shui Tian et al. Human brain mapping 2023
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Predictors of suicide ideation among South Korean adolescents: A machine learning approach.
Hayoung Kim et al. Journal of affective disorders 2023
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Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review.
Elias Hossain et al. Computers in biology and medicine 2023 155106649
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Prediction of Suicidal Behaviors in the Middle-aged Population: Machine Learning Analyses of UK Biobank.
Junren Wang et al. JMIR public health and surveillance 2023 9e43419
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Application of Natural Language Processing (NLP) in Detecting and Preventing Suicide Ideation: A Systematic Review.
Arowosegbe Abayomi et al. International journal of environmental research and public health 2023 20(2)
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Page last reviewed:
Feb 1, 2024
Page last updated:
Apr 23, 2024
Content source:
Public Health Genomics Branch in the Division of Blood Disorders and Public Health Genomics
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National Center on Birth Defects and Developmental Disabilities
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