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Development and validation of a machine learning‐based postpartum depression prediction model: A nationwide cohort study
Depression and Anxiety ( IF 4.7 ) Pub Date : 2020-12-07 , DOI: 10.1002/da.23123
Eldar Hochman 1, 2, 3 , Becca Feldman 4 , Abraham Weizman 1, 2, 3 , Amir Krivoy 1, 2, 3, 5 , Shay Gur 1, 2 , Eran Barzilay 6, 7 , Hagit Gabay 4 , Joseph Levy 4 , Ohad Levinkron 4 , Gabriella Lawrence 4, 8
Affiliation  

Currently, postpartum depression (PPD) screening is mainly based on self‐report symptom‐based assessment, with lack of an objective, integrative tool which identifies women at increased risk, before the emergent of PPD. We developed and validated a machine learning‐based PPD prediction model utilizing electronic health record (EHR) data, and identified novel PPD predictors.

中文翻译:

基于机器学习的产后抑郁预测模型的开发和验证:一项全国性队列研究

目前,产后抑郁症(PPD)筛查主要基于自我报告症状评估,缺乏在PPD出现之前识别风险增加的女性的客观,综合工具。我们利用电子健康记录(EHR)数据开发并验证了基于机器学习的PPD预测模型,并确定了新颖的PPD预测因子。
更新日期:2020-12-07
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