Design Comorbidity Portfolios to Improve Treatment Cost Prediction of Asthma Using Machine Learning

Abstract

Comorbidity is an important factor to consider when trying to predict the cost of treating asthma patients. When an asthmatic patient suffered from comorbidity, the cost of treating such a patient becomes dependent on the nature of the comorbidity. Therefore, lack of recognition of comorbidity on asthmatic patient poses a challenge in predicting the cost of treatment. In this study, we proposed a comorbidity portfolio design that improves the prediction cost of treating asthmatic patients by regrouping frequently occurred comorbidities in different cost groups. In the experiment, predictive models, including logistic regression, random forest, support vector machine, classification regression tree, and backpropagation neural network were trained with real-world data of asthmatic patients from 2012 to 2014 in a large city of China. The 10-fold cross validation and random search algorithm were employed to optimize the hyper-parameters. We recorded significant improvements using our model, which are attributed to comorbidity portfolios in area under curve (AUC) and sensitivity increase of 46.89% (standard deviation: 4.45%) and 101.07% (standard deviation: 44.94%), respectively. In risk analysis of comorbidity on cost, respiratory diseases with a cumulative proportion in the adjusted odds ratio of 36.38% (95%CI: 27.61%, 47.86%) and circulatory diseases with a cumulative proportion in the adjusted odds ratio of 23.83% (95%CI: 15.95%, 35.22%) are the dominant risks of asthmatic patients that affects the treatment cost. It is found that the comorbidity portfolio is robust, and provides a better prediction of the high-cost of treating asthmatic patients. The preliminary characterization of the joint risk of multiple comorbidities posed on cost are also reported. This study will be of great help in improving cost prediction and comorbidity management.