Predicting postpartum psychiatric admission using a machine learning approach
Predicting postpartum psychiatric admission using a machine learning approach
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DOI：
10.1016/j.jpsychires.2020.07.002
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发表时间：
2020-11-01
影响因子：
4.8
通讯作者：
Alati, Rosa
中科院分区：
文献类型：
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作者：
Betts, Kim S.;Kisely, Steve;Alati, Rosa
目的：准确识别有产后精神病入院风险的母亲将有助于预防性干预或更及时入院。我们开发了一个预测模型来识别有产后精神病入院风险的女性。方法：数据包括 2009 年 1 月至 2014 年 10 月期间澳大利亚昆士兰州所有住院活产婴儿的管理健康数据。分析仅限于在怀孕期间出现一项或多项心理健康问题指标的母亲（n = 75,054 名新生儿）。预测因素包括分娩前的所有孕产妇数据以及分娩时记录的新生儿数据。我们使用多种机器学习方法来预测产后 12 个月内的入院情况，其中初步诊断被记录为 ICD-10 精神病、双相情感障碍或抑郁症。结果：提升树算法产生了性能最佳的模型，在验证数据中预测产后精神病入院具有良好的辨别力 [AUC = 0.80； 95% CI = (0.76, 0.83)]并实现良好的校准。该模型优于基准逻辑回归模型和弹性网络模型。除了孕产妇金属健康史指标外，孕产妇和新生儿人体测量指标以及社会/生活方式因素也是强有力的预测因素。结论：我们的结果表明，在识别有产后精神病入院风险的母亲时，大数据方法的潜力。新生儿出院后，可以对处于危险中的母亲进行随访和支持，以消除入院的需要或促进更及时的入院。
Aims: The accurate identification of mothers at risk of postpartum psychiatric admission would allow for preventive intervention or more timely admission. We developed a prediction model to identify women at risk of postpartum psychiatric admission.Methods: Data included administrative health data of all inpatient live births in the Australian state of Queensland between January 2009 and October 2014. Analyses were restricted to mothers with one or more indicator of mental health problems during pregnancy (n = 75,054 births). The predictors included all maternal data up to and including the delivery, and neonatal data recorded at delivery. We used multiple machine learning methods to predict hospital admission in the 12 months following delivery in which the primary diagnosis was recorded as an ICD-10 psychotic, bipolar or depressive disorders.Results: The boosted trees algorithm produced the best performing model, predicting postpartum psychiatric admission in the validation data with good discrimination [AUC = 0.80; 95% CI = (0.76, 0.83)] and achieving good calibration. This model outperformed benchmark logistic regression model and an elastic net model. In addition to indicators of maternal metal health history, maternal and neonatal anthropometric measures and social/lifestyle factors were strong predictors.Conclusion: Our results indicate the potential of a big data approach when aiming to identify mothers at risk of postpartum psychiatric admission. Mothers at risk could be followed-up and supported after neonatal discharge to either remove the need for admission or facilitate more timely admission.