Development and validation of a machine learning‐based postpartum depression prediction model: A nationwide cohort study,Depression and Anxiety

当前位置： X-MOL 学术 › Depression Anxiety › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

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：