Machine learning prediction models for postpartum depression: A multicenter study in Japan
Machine learning prediction models for postpartum depression: A multicenter study in Japan
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DOI：
10.1111/jog.15266
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发表时间：
2022-04
影响因子：
1.6
通讯作者：
Seiko Matsuo;T. Ushida;R. Emoto;Y. Moriyama;Yukako Iitani;Noriyuki Nakamura;K. Imai;Tomoko Nakano-Kobayashi;S. Yoshida;Mamoru Yamashita;S. Matsui;H. Kajiyama;Tomomi Kotani
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文献类型：
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作者：
Seiko Matsuo;T. Ushida;R. Emoto;Y. Moriyama;Yukako Iitani;Noriyuki Nakamura;K. Imai;Tomoko Nakano-Kobayashi;S. Yoshida;Mamoru Yamashita;S. Matsui;H. Kajiyama;Tomomi Kotani
产后抑郁症（PPD）和围产期精神卫生保健在世界范围内越来越重要。在这里，我们的目标是开发和验证用于预测PPD的机器学习模型，并评估日本部分地区最近采用的2周产后检查对识别PPD高风险女性的有用性。
Postpartum depression (PPD) and perinatal mental health care are of growing importance worldwide. Here we aimed to develop and validate machine learning models for the prediction of PPD, and to evaluate the usefulness of the recently adopted 2‐week postpartum checkup in some parts of Japan for the identification of women at high risk of PPD.