Predicting postpartum psychiatric admission using a machine learning approach

Kim S. Betts; Steve Kisely; Rosa Alati

doi:10.1016/j.jpsychires.2020.07.002

Predicting postpartum psychiatric admission using a machine learning approach

Kim S. Betts, Steve Kisely, Rosa Alati

科研成果: 期刊稿件 › 文章 › 同行评审

源语言	英语
页（从-至）	35-40
页数	6
期刊	Journal of Psychiatric Research
卷	130
DOI	https://doi.org/10.1016/j.jpsychires.2020.07.002
出版状态	已出版 - 11月 2020
已对外发布	是

!!!ASJC Scopus Subject Areas

精神病学和心理健康
生物精神病学

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10.1016/j.jpsychires.2020.07.002

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@article{3409fa27a11648b6aef6926343da9aad,

title = "Predicting postpartum psychiatric admission using a machine learning approach",

author = "Betts, {Kim S.} and Steve Kisely and Rosa Alati",

note = "Funding Information: All data was provided free of charge by Queenslad Health, and no funding was required as part of this study. Publisher Copyright: {\textcopyright} 2020",

year = "2020",

month = nov,

doi = "10.1016/j.jpsychires.2020.07.002",

language = "English",

volume = "130",

pages = "35--40",

journal = "Journal of Psychiatric Research",

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AU - Kisely, Steve

AU - Alati, Rosa

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SN - 0022-3956

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SP - 35

EP - 40

JO - Journal of Psychiatric Research

JF - Journal of Psychiatric Research

ER -

Predicting postpartum psychiatric admission using a machine learning approach

!!!ASJC Scopus Subject Areas

联合国可持续发展目标

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