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Public Health Genomics and Precision Health Knowledge Base (v9.0)
PHGKB
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Public Health Genomics Branch
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Precision Health Database|Search|Public Health Genomics and Precision Health Knowledge Base (PHGKB)
Last data update: Apr 25, 2024
. (Total: 63675 Documents since 2012)
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Records 1 - 17 (of 17 Records)
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Identifying dementia from cognitive footprints in hospital records among Chinese older adults: a machine-learning study.
Jiayi Zhou et al. Lancet Reg Health West Pac 2024 46101060
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Who is at risk for weight gain after weight-gain associated treatment with antipsychotics, antidepressants, and mood stabilizers: A machine learning approach.
Julia Eder et al. Acta Psychiatr Scand 2024
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A risk identification model for detection of patients at risk of antidepressant discontinuation.
Ali Zolnour et al. Front Artif Intell 2023 61229609
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AI-assisted prediction of differential response to antidepressant classes using electronic health records.
Yi-Han Sheu et al. NPJ Digit Med 2023 6(1) 73
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Initial antidepressant choice by non-psychiatrists: Learning from large-scale electronic health records.
Yi-Han Sheu et al. General hospital psychiatry 2023 8122-31
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Development of a model to predict combined antidepressant medication and psychotherapy treatment response for depression among veterans.
Robert M Bossarte et al. Journal of affective disorders 2023
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Predicting treatment response using EEG in major depressive disorder: A machine-learning meta-analysis.
Watts Devon et al. Translational psychiatry 2022 12(1) 332
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Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants.
Athreya Arjun P et al. Journal of child psychology and psychiatry, and allied disciplines 2022
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A class-contrastive human-interpretable machine learning approach to predict mortality in severe mental illness.
Banerjee Soumya et al. NPJ schizophrenia 2021 7(1) 60
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Multi-omics driven predictions of response to acute phase combination antidepressant therapy: a machine learning approach with cross-trial replication.
Joyce Jeremiah B et al. Translational psychiatry 2021 11(1) 513
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Sex-Specific Risk Profiles for Suicide Among Persons with Substance Use Disorders in Denmark.
Adams Rachel Sayko et al. Addiction (Abingdon, England) 2021
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Combining machine-learning algorithms for prediction of antidepressant treatment response.
Kautzky A et al. Acta psychiatrica Scandinavica 2020 Nov
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Predicting second-generation antidepressant effectiveness in treating sadness using demographic and clinical information: A machine learning approach.
Lin Amanda et al. Journal of affective disorders 2020 Jul 272295-304
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Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression: A Prespecified Secondary Analysis of a Randomized Clinical Trial.
Rajpurkar Pranav et al. JAMA network open 2020 Jun 3(6) e206653
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Personalise antidepressant treatment for unipolar depression combining individual choices, risks and big data (PETRUSHKA): rationale and protocol.
Tomlinson Anneka et al. Evidence-based mental health 2019 Oct
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Using Electronic Health Records and Machine Learning to Predict Postpartum Depression.
Wang Shuojia et al. Studies in health technology and informatics 2019 Aug 264888-892
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Development of Machine Learning Algorithms for Prediction of Sustained Postoperative Opioid Prescriptions After Total Hip Arthroplasty.
Karhade Aditya V et al. The Journal of arthroplasty 2019 Jun
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Page last reviewed:
Feb 1, 2024
Page last updated:
Apr 25, 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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