GWAS-BASED MACHINE LEARNING APPROACH TO PREDICT DULOXETINE RESPONSE AND REMISSION IN MAJOR DEPRESSIVE DISORDER

被引：0
|
作者：
Maciukiewicz, Malgorzata [1 ]
Marshe, Victoria [1 ]
Hauschild, Anne-Christine [2 ]
Foster, Jane A. [3 ]
Rotzinger, Susan [3 ]
Kennedy, James L. [1 ]
Kennedy, Sidney H. [4 ]
Mueller, Daniel J. [1 ]
Geraci, Joseph [5 ]
机构：
[1] Ctr Addict & Mental Hlth, Toronto, ON, Canada
[2] Univ Toronto, Univ Hlth Network, Princess Margaret Canc Ctr, IBM Life Sci Discovery Ctr, Toronto, ON, Canada
[3] Univ Hlth Network, Toronto, ON, Canada
[4] Univ Toronto, Univ Hlth Network, Toronto, ON, Canada
[5] St Michaels Hosp, Toronto, ON, Canada
来源：
EUROPEAN NEUROPSYCHOPHARMACOLOGY | 2019年 / 29卷
关键词：
D O I：
10.1016/j.euroneuro.2017.08.110
中图分类号：
R74 [神经病学与精神病学];
学科分类号：
摘要：
SA38
引用
收藏
页码：S843 / S843
页数：1
相关论文
共 50 条
  • [1] GWAS-BASED MACHINE LEARNING APPROACH TO PREDICT DULOXETINE RESPONSE IN MAJOR DEPRESSIVE DISORDER
    Maciukiewicz, Malgorzata
    Geraci, Joseph
    Rotzinger, Susan
    Foster, Jane A.
    Kennedy, James L.
    Mueller, Daniel J.
    EUROPEAN NEUROPSYCHOPHARMACOLOGY, 2017, 27 : S285 - S286
  • [2] GWAS-based machine learning approach to predict duloxetine response in major depressive disorder
    Maciukiewicz, Malgorzata
    Marshe, Victoria S.
    Hauschild, Anne-Christin
    Foster, Jane A.
    Rotzinger, Susan
    Kennedy, James L.
    Kennedy, Sidney H.
    Mueller, Daniel J.
    Geraci, Joseph
    JOURNAL OF PSYCHIATRIC RESEARCH, 2018, 99 : 62 - 68
  • [3] Prediction of repeated-dose intravenous ketamine response in major depressive disorder using the GWAS-based machine learning approach
    Bao, Zhiwei
    Zhao, Xinyi
    Li, Jingjing
    Zhang, Guanghua
    Wu, Hairong
    Ning, Yuping
    Li, Ming D.
    Yang, Zhongli
    JOURNAL OF PSYCHIATRIC RESEARCH, 2021, 138 : 284 - 290
  • [4] BLOOD-BASED BIOMARKERS TO PREDICT MAJOR DEPRESSIVE DISORDER PHENOTYPES: A MACHINE LEARNING APPROACH
    Gentry, Amanda
    Singh, MadhurBain
    Mohammed, Hassan
    Peterson, Roseann E.
    Todd, Webb B.
    EUROPEAN NEUROPSYCHOPHARMACOLOGY, 2023, 75 : S176 - S176
  • [5] A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder
    Khodayari-Rostamabad, Ahmad
    Reilly, James P.
    Hasey, Gary M.
    de Bruin, Hubert
    MacCrimmon, Duncan J.
    CLINICAL NEUROPHYSIOLOGY, 2013, 124 (10) : 1975 - 1985
  • [6] Remission rates during treatment with duloxetine in patients with major depressive disorder
    Lu, Y
    Tran, P
    Detke, M
    EUROPEAN NEUROPSYCHOPHARMACOLOGY, 2002, 12 : S214 - S214
  • [7] Machine Learning Approaches to Predict Repetitive Transcranial Magnetic Stimulation Treatment Response in Major Depressive Disorder
    Erguzel, Turker Tekin
    Tarhan, Nevzat
    PROCEEDINGS OF SAI INTELLIGENT SYSTEMS CONFERENCE (INTELLISYS) 2016, VOL 2, 2018, 16 : 391 - 401
  • [8] Using machine-learning to predict sudden gains in treatment for major depressive disorder
    Aderka, Idan M.
    Kauffmann, Amitay
    Shalom, Jonathan G.
    Beard, Courtney
    Bjorgvinsson, Throstur
    BEHAVIOUR RESEARCH AND THERAPY, 2021, 144
  • [9] An EEG and Machine Learning based Method for the Detection of Major Depressive Disorder
    Izci, Elif
    Ozdemir, Mehmet Akif
    Akan, Aydin
    Ozcoban, Mehmet Akif
    Arikan, Mehmet Kemal
    29TH IEEE CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS (SIU 2021), 2021,
  • [10] Remission, response without remission, and nonresponse in major depressive disorder: impact on functioning
    Trivedi, Madhukar H.
    Corey-Lisle, Patricia K.
    Guo, Zhenchao
    Lennox, Richard D.
    Pikalov, Andrei
    Kim, Edward
    INTERNATIONAL CLINICAL PSYCHOPHARMACOLOGY, 2009, 24 (03) : 133 - 138
12345