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Comparing Artificial Intelligence and Traditional Methods to Identify Factors Associated With Pediatric Asthma
Alexander H Hogan1, Michael Brimacombe2, Maua Mosha2
1Division of Hospital Medicine, Connecticut Children's Medical Center (AH Hogan), Hartford, Conn; Department of Pediatrics, University of Connecticut School of Medicine (AH Hogan), Farmington, Conn.
Insights
Artificial neural networks identify more pediatric asthma readmission risk factors than traditional models. This advanced approach reveals complex interactions, improving prediction accuracy for better asthma management.
Area of Science:
- Pediatric Pulmonology
- Health Informatics
- Biostatistics
Background:
- Pediatric asthma readmissions pose a significant healthcare burden.
- Identifying accurate risk factors is crucial for effective intervention strategies.
Purpose of the Study:
- To compare traditional statistical models with artificial neural networks for identifying pediatric asthma readmission risk factors.
- To contrast the predictive capabilities of Cox proportional-hazards, logistic regression, and neural network models for 180-day readmissions.
Main Methods:
- Retrospective cohort study using the 2013 Nationwide Readmissions Database.
- Inclusion of pediatric patients (5-18 years) with primary asthma diagnosis.
- Comparison of logistic regression and neural network models using area under the receiver-operating curve (AUC).
Main Results:
- Neural networks identified 9 significant risk factors, including age, hospital characteristics, and complex chronic conditions, some unique from traditional models.
- Traditional models identified longer length of stay, public insurance, and non-winter admissions as risk factors, with micropolitan counties being protective.
- Neural network model (AUC=0.637) outperformed logistic regression (AUC=0.592) in predicting readmissions.
Conclusions:
- Artificial neural networks offer a more comprehensive approach to identifying pediatric asthma readmission risk factors compared to traditional models.
- Traditional modeling alone may miss crucial risk factors and complex interactions identified by neural networks.
- Advanced modeling techniques like neural networks can enhance the accuracy of readmission risk prediction in pediatric asthma.
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.
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