Last Posted: Jun 22, 2021
- Detecting Suicide and Self-Harm Discussions Among Opioid Substance Users on Instagram Using Machine Learning.
Purushothaman Vidya et al. Frontiers in psychiatry 2021 12551296
- Pharmacogenetic Associations Between Atazanavir/UGT1A1*28 and Efavirenz/rs3745274 (CYP2B6) Account for Specific Adverse Reactions in Chilean Patients Undergoing Antiretroviral Therapy.
Poblete Daniela et al. Frontiers in pharmacology 2021 12660965
- Patient and Relative Experiences and Decision-making About Genetic Testing and Counseling for Familial ALS and FTD: A Systematic Scoping Review.
Crook Ashley et al. Alzheimer disease and associated disorders 2021
- Convolutional Neural Network-Based Deep Learning Model for Predicting Differential Suicidality in Depressive Patients Using Brain Generalized q-Sampling Imaging.
Chen Vincent Chin-Hung et al. The Journal of clinical psychiatry 2021 82(2)
- A prospective study to determine the clinical utility of pharmacogenetic testing of veterans with treatment-resistant depression.
McCarthy Michael J et al. Journal of psychopharmacology (Oxford, England) 2021 2698811211015224
- User-Centered Design of a Machine Learning Intervention for Suicide Risk Prediction in a Military Setting.
Reale Carrie et al. AMIA ... Annual Symposium proceedings. AMIA Symposium 2021 20201050-1058
- Troubling Neurobiological Vulnerability: Psychiatric Risk and the Adverse Milieu in Environmental Epigenetics Research.
Filipe Angela Marques et al. Frontiers in sociology 2021 6635986
- Predicting Sex-Specific Non-Fatal Suicide Attempt Risk Using Machine Learning and Data from Danish National Registries.
Gradus Jaimie L et al. American journal of epidemiology 2021
- Impact of Big Data Analytics on People's Health: Overview of Systematic Reviews and Recommendations for Future Studies.
Borges do Nascimento Israel Júnior et al. Journal of medical Internet research 2021 23(4) e27275
- Using General-purpose Sentiment Lexicons for Suicide Risk Assessment in Electronic Health Records: Corpus-Based Analysis.
Bittar André et al. JMIR medical informatics 2021 9(4) e22397
- Natural language processing and machine learning of electronic health records for prediction of first-time suicide attempts.
Tsui Fuchiang R et al. JAMIA open 2021 4(1) ooab011
- Prospective Validation of an Electronic Health Record–Based, Real-Time Suicide Risk Model
CG Walsh et al, JAMA Network Open, March 2021
- Machine Learning Assessment of Early Life Factors Predicting Suicide Attempt in Adolescence or Young Adulthood.
Navarro Marie C et al. JAMA network open 2021 4(3) e211450
- Patient perspectives on acceptability of, and implementation preferences for, use of electronic health records and machine learning to identify suicide risk.
Yarborough Bobbi Jo H et al. General hospital psychiatry 2021 7031-37
- Reconciling Statistical and Clinicians' Predictions of Suicide Risk.
Simon Gregory E et al. Psychiatric services (Washington, D.C.) 2021 appips202000214
- Using machine learning to predict suicide in the 30 days after discharge from psychiatric hospital in Denmark.
Jiang Tammy et al. The British journal of psychiatry : the journal of mental science 2021 1-8
- Sex-Specific Risk Profiles for Suicide Among Persons with Substance Use Disorders in Denmark.
Adams Rachel Sayko et al. Addiction (Abingdon, England) 2021
- Computerized screening may help identify youth at risk for suicide
NIH, February 23, 2021
- A machine learning approach predicts future risk to suicidal ideation from social media data.
Roy Arunima et al. NPJ digital medicine 2020 May 3(1) 78
- Identifying intentional injuries among children and adolescents based on Machine Learning.
Yin Xiling et al. PloS one 2021 16(1) e0245437
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