Predicting neonatal respiratory distress syndrome and hypoglycaemia prior to discharge: Leveraging health administrative data and machine learning,Journal of Biomedical informatics

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Predicting neonatal respiratory distress syndrome and hypoglycaemia prior to discharge: Leveraging health administrative data and machine learning
Journal of Biomedical informatics ( IF 4.0 ) Pub Date : 2020-12-05 , DOI: 10.1016/j.jbi.2020.103651
Kim S Betts ₁ , Steve Kisely ₂ , Rosa Alati ₁

Affiliation

Objectives

A major challenge for hospitals and clinicians is the early identification of neonates at risk of developing adverse conditions. We develop a model based on routinely collected administrative data, which accurately predicts two common disorders among early term and preterm (<39 weeks) neonates prior to discharge.

Study design.

The data included all inpatient live births born prior to 39 weeks (n = 154,755) occurring in the Australian state of Queensland between January 2009 and December 2015. Predictor variables included all maternal data captured in administrative records from the beginning of gestation up to, and including, the delivery, as well as neonatal data recorded at the delivery. Gradient boosted trees were used to predict neonatal respiratory distress syndrome and hypoglycaemia prior to discharge, with model performance benchmarked against a logistic regression models.

Results

The gradient boosted trees model achieved very high discrimination for respiratory distress syndrome [AUC = 0.923, 95% CI (0.917, 0.928)] and good discrimination for hypoglycaemia [AUC = 0.832, 95% CI (0.827, 0.837)] in the validation data, as well as outperforming the logistic regression models.

Conclusion

Our study suggests that routinely collected health data have the potential to play an important role in assisting clinicians to identify neonates at risk of developing selected disorders shortly after birth. Despite achieving high levels of discrimination, many issues remain before such models can be implemented in practice, which we discuss in relation to our findings.

中文翻译：

出院前预测新生儿呼吸窘迫综合征和低血糖：利用卫生管理数据和机器学习

目标

医院和临床医生面临的主要挑战是及早发现具有发展为不利条件的风险的新生儿。我们基于常规收集的管理数据开发了一个模型，该模型可以准确预测出院前的早产和早产（<39周）新生儿中的两种常见疾病。

学习规划。

数据包括2009年1月至2015年12月在澳大利亚昆士兰州发生的39周之前出生的所有住院活产婴儿（n = 154,755）。预测变量包括从妊娠开始到出生的行政记录中捕获的所有产妇数据，以及包括分娩以及分娩时记录的新生儿数据。使用梯度增强树在出院前预测新生儿呼吸窘迫综合征和低血糖，模型性能以逻辑回归模型为基准。