Comparing Artificial Intelligence and Traditional Methods to Identify Factors Associated With Pediatric Asthma Readmission
Comparing Artificial Intelligence and Traditional Methods to Identify Factors Associated With Pediatric Asthma Readmission
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DOI：
10.1016/j.acap.2021.07.015
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发表时间：
2022-01-01
影响因子：
3.1
通讯作者：
Flores, Glenn
中科院分区：
文献类型：
--
作者：
Hogan, Alexander H.;Brimacombe, Michael;Flores, Glenn
目的：使用传统模型（考克斯比例风险和逻辑回归）和人工神经网络modeling.METHODS：2013年全国再入院数据库的回顾性队列研究包括5至18岁的儿童，主要诊断为哮喘。主要结局是考克斯模型中的哮喘再入院时间和逻辑回归中180天内的再入院时间。具有2个隐藏层和多个重复的基本神经网络构造考虑了所有数据集变量和潜在变量相互作用，以预测180天的再入院。Logistic回归和神经网络模型在受试者工作曲线下面积上进行了比较。结果：在18,489例儿童哮喘住院患者中，1858例在180天内再次入院。在考克斯和Logistic模型中，较长的住院时间、公共保险和非冬季住院季节与再入院风险相关，而小都市县具有保护作用。在神经网络模型中，9个因素与再入院显著相关。四个与考克斯模型重叠（非冬季月入院，住院时间长，公共保险和小城市医院），而5个是独特的（年龄，医院病床数，教学医院的地位，周末指数入院，和复杂的慢性病）。逻辑回归的曲线下面积为0.592，神经网络的曲线下面积为0.637。结论：不同的方法可以产生不同的再入院模型。仅仅依靠传统的建模，忽略了关键的再入院风险因素和神经网络识别的复杂因素相互作用。
OBJECTIVE: To identify and contrast risk factors for six-month pediatric asthma readmissions using traditional models (Cox proportional-hazards and logistic regression) and artificial neural-network modeling.METHODS: This retrospective cohort study of the 2013 Nationwide Readmissions Database included children 5 to 18 years old with a primary diagnosis of asthma. The primary outcome was time to asthma readmission in the Cox model, and readmission within 180 days in logistic regression. A basic neural network construction with 2 hidden layers and multiple replications considered all dataset variables and potential variable interactions to predict 180-day readmissions. Logistic regression and neural-network models were compared on area under-the receiver-operating curve.RESULTS: Of 18,489 pediatric asthma hospitalizations, 1858 were readmitted within 180 days. In Cox and logistic models, longer index length of stay, public insurance, and nonwinter index admission seasons were associated with readmission risk, whereas micropolitan county was protective. In neural network modeling, 9 factors were significantly associated with readmissions. Four overlapped with the Cox model (nonwinter-month admission, long length of stay, public insurance, and micropolitan hospitals), whereas 5 were unique (age, hospital bed number, teaching-hospital status, weekend index admission, and complex chronic conditions). The area under the curve was 0.592 for logistic regression and 0.637 for the neural network.CONCLUSIONS: Different methods can produce different readmission models. Relying on traditional modeling alone overlooks key readmission risk factors and complex factor interactions identified by neural networks.