1. Applying Machine Learning Models to Predict Medication Nonadherence in Crohn’s Disease Maintenance Therapy
- Author
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Wang, Lei, Fan, Rong, Zhang, Chen, Hong, Liwen, Zhang, Tianyu, Chen, Ying, Liu, Kai, Wang, Zhengting, and Zhong, Jie
- Subjects
Crohn’s disease ,azathioprine ,machine learning ,medication adherence ,maintenance therapy ,back-propagation neural network ,support vector machine ,Original Research - Abstract
Objective Medication adherence is crucial in the management of Crohn’s disease (CD), and yet the adherence remains low. This study aimed to develop machine learning models that can help predict CD patients of nonadherence to azathioprine (AZA), and thus assist caregivers to streamline the intervention process. Methods This single-centered, cross-sectional study recruited 446 CD patients who have been prescribed AZA between Sep 2005 and Sep 2018. Questionnaires of medication adherence, anxiety and depression, beliefs of medication necessity and concerns, and medication knowledge were provided to patients, while other data were extracted from the electronic medical records. Two machine learning models of back-propagation neural network (BPNN) and support vector machine (SVM) were developed and compared with logistic regression (LR), and assessed by accuracy, recall, precision, F1 score and the area under the receiver operating characteristic curve (AUC). Results The average classification accuracy and AUC of the three models were 81.6% and 0.896 for LR, 85.9% and 0.912 for BPNN, and 87.7% and 0.930 for SVM, respectively. Multivariate analysis identified four risk factors associated with AZA nonadherence: medication concern belief (OR=3.130, p
- Published
- 2020