ArticlePatient preference and adherence2020
Applying Machine Learning Models to Predict Medication Nonadherence in Crohn's Disease Maintenance Therapy.
Article in Patient preference and adherence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation.Inflammatory bowel diseases · 2022Pooled it
- Predicting self-administered biologic nonadherence in Medicare fee-for-service beneficiaries with inflammatory bowel disease using machine learning models.Journal of managed care & specialty pharmacy · 2026Article
- Development and Validation of a Machine Learning Model for Predicting Medication Adherence Among Home-Dwelling Elderly Patients: A Retrospective Cross-Sectional Study.Patient preference and adherence · 2026Article
- Machine learning approaches to predicting medication nonadherence: a scoping review.International journal of medical informatics · 2025Article
- Medication Non-Adherence in Inflammatory Bowel Disease: A Systematic Review Identifying Risk Factors and Opportunities for Intervention.Pharmacy (Basel, Switzerland) · 2025Review
- Artificial intelligence use for precision medicine in inflammatory bowel disease: a systematic review.American journal of translational research · 2025Review
- Digital biomarkers and artificial intelligence: a new frontier in personalized management of inflammatory bowel disease.Frontiers in immunology · 2025Review
- Machine learning applications to classify and monitor medication adherence in patients with type 2 diabetes in Ethiopia.Frontiers in endocrinology · 2025Article
- Explainable AI-prioritized plasma and fecal metabolites in inflammatory bowel disease and their dietary associations.iScience · 2024Article
- Applying Machine Learning Models Derived From Administrative Claims Data to Predict Medication Nonadherence in Patients Self-Administering Biologic Medications for Inflammatory Bowel Disease.Crohn's & colitis 360 · 2024Article
- Article
- A comprehensive secure system enabling healthcare 5.0 using federated learning, intrusion detection and blockchain.PeerJ. Computer science · 2024Article
- Sequential model for predicting patient adherence in subcutaneous immunotherapy for allergic rhinitis.Frontiers in pharmacology · 2024Article
- A novel hybrid model to predict concomitant diseases for Hashimoto's thyroiditis.BMC bioinformatics · 2023Article
- Development and economic assessment of machine learning models to predict glycosylated hemoglobin in type 2 diabetes.Frontiers in pharmacology · 2023Article
- Medication Non-Adherence in Rheumatology, Oncology and Cardiology: A Review of the Literature of Risk Factors and Potential Interventions.International journal of environmental research and public health · 2022Review
- Use of machine learning to identify patients at risk of sub-optimal adherence: study based on real-world data from 10,929 children using a connected auto-injector device.BMC medical informatics and decision making · 2022Article
- Machine Learning Predictive Outcomes Modeling in Inflammatory Bowel Diseases.Inflammatory bowel diseases · 2022Review
- Application of Artificial Intelligence to Clinical Practice in Inflammatory Bowel Disease - What the Clinician Needs to Know.Journal of Crohn's & colitis · 2022Article
- Integrated Digital Health Solutions in the Management of Growth Disorders in Pediatric Patients Receiving Growth Hormone Therapy: A Retrospective Analysis.Frontiers in endocrinology · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveMedication 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.
methodsThis 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).
resultsThe 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<0.001), education (OR=2.199, p<0.001), anxiety (OR=1.549, p<0.001) and depression (OR=1.190, p<0.001), while medication necessity belief (OR=0.004, p<0.001) and medication knowledge (OR=0.805, p=0.013) were protective factors.
conclusionWe developed three machine learning models and proposed an SVM model with promising accuracy in the prediction of AZA nonadherence in Chinese CD patients. The study also reconfirmed that education, psychologic distress, and medication beliefs and knowledge are correlated to AZA nonadherence.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.