Comparing Artificial Intelligence and Traditional Methods to Identify Factors Associated With Pediatric Asthma Readmission

Abstract

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.

Keywords: artificial neural network; asthma; machine learning; rehospitalization.