Machine learning in the prediction of depression treatment outcomes: A systematic review and meta-analysis

doi:10.1017/S0033291721003871

Machine learning in the prediction of depression treatment outcomes: A systematic review and meta-analysis

Research output: Contribution to journal › Review article › peer-review

Original language	English
Pages (from-to)	2742-2751
Number of pages	10
Journal	Psychological Medicine
Volume	51
Issue number	16
DOIs	https://doi.org/10.1017/S0033291721003871
Publication status	Published - Dec 12 2021
Externally published	Yes

ASJC Scopus Subject Areas

Psychiatry and Mental health
Applied Psychology

Keywords

MDD
Machine learning
meta-analysis
predictive analysis
systematic review
treatment outcome

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1017/S0033291721003871

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title = "Machine learning in the prediction of depression treatment outcomes: A systematic review and meta-analysis",

keywords = "MDD, Machine learning, meta-analysis, predictive analysis, systematic review, treatment outcome",

note = "Publisher Copyright: Copyright {\textcopyright} 2021 The Author(s). Published by Cambridge University Press.",

year = "2021",

month = dec,

day = "12",

doi = "10.1017/S0033291721003871",

language = "English",

volume = "51",

pages = "2742--2751",

journal = "Psychological Medicine",

issn = "0033-2917",

publisher = "Cambridge University Press",

number = "16",

}

TY - JOUR

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T2 - A systematic review and meta-analysis

PY - 2021/12/12

Y1 - 2021/12/12

KW - MDD

KW - Machine learning

KW - meta-analysis

KW - predictive analysis

KW - systematic review

KW - treatment outcome

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U2 - 10.1017/S0033291721003871

DO - 10.1017/S0033291721003871

M3 - Review article

AN - SCOPUS:85117175608

SN - 0033-2917

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SP - 2742

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JO - Psychological Medicine

JF - Psychological Medicine

IS - 16

ER -

Machine learning in the prediction of depression treatment outcomes: A systematic review and meta-analysis

ASJC Scopus Subject Areas

Keywords

UN SDGs

Access to Document

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