ArticleScientific reports2021
Predicting medication adherence using ensemble learning and deep learning models with large scale healthcare data.
Article in Scientific reports, 2021. 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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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence-based tools for patient support to enhance medication adherence: a focused review.Frontiers in digital health · 2025Pooled it
- Challenge in Predicting Persistence to P2Y12 Inhibitors: A Perspective From the ARTEMIS Trial.Journal of the American Heart Association · 2023Trial
- Large language model-based triage to identify antiretroviral therapy adherence barriers and risk levels in patient messages.JAMIA open · 2026Article
- Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.Journal of medical Internet research · 2026Article
- Artificial intelligence approaches to predicting treatment non-adherence in chronic diseases: a narrative review.Frontiers in digital health · 2026Review
- 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
- Multimodal Sensing and Modeling of Endocrine Therapy Adherence in Breast Cancer Survivors.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2025Article
- Machine learning approaches to predicting medication nonadherence: a scoping review.International journal of medical informatics · 2025Article
- Personalized and real time hemodynamic management in critical care using Dynamic Cohort Ensemble Learning (DynaCEL).NPJ digital medicine · 2025Article
- Construction of Personalized Predictive Models for Missed Medication Doses Using Wearable Device Data: Prospective Observational Study.JMIR formative research · 2025Observational
- A computational framework for longitudinal medication adherence prediction in breast cancer survivors: A social cognitive theory based approach.PLOS digital health · 2025Article
- Predictors and implications of medication non-adherence among psychiatric patients in Oman: A cross-sectional study.Qatar medical journal · 2025Article
- Machine learning applications to classify and monitor medication adherence in patients with type 2 diabetes in Ethiopia.Frontiers in endocrinology · 2025Article
- Predictive Modeling of Factors Influencing Adherence to SGLT-2 Inhibitors in Ambulatory Care: Insights from Prescription Claims Data Analysis.Pharmacy (Basel, Switzerland) · 2024Article
- Inaccurately Reported Statin Use Affects the Assessing of Lipid Profile Measures and Their Association with Coronary Artery Disease Risk.Clinical chemistry · 2024Article
- A Review of Sensor-Based Interventions for Supporting Patient Adherence to Inhalation Therapy.Patient preference and adherence · 2024Review
- Opioid Nonadherence Risk Prediction of Patients with Cancer-Related Pain Based on Five Machine Learning Algorithms.Pain research & management · 2024Article
- Sequential model for predicting patient adherence in subcutaneous immunotherapy for allergic rhinitis.Frontiers in pharmacology · 2024Article
- Comprehensive hepatotoxicity prediction: ensemble model integrating machine learning and deep learning.Frontiers in pharmacology · 2024Article
- Predicting Adherence to Behavior Change Support Systems Using Machine Learning: Systematic Review.JMIR AI · 2023Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clinical studies from WHO have demonstrated that only 50-70% of patients adhere properly to prescribed drug therapy. Such adherence failure can impact therapeutic efficacy for the patients in question and compromises data quality around the population-level efficacy of the drug for the indications targeted. In this study, we applied various ensemble learning and deep learning models to predict medication adherence among patients. Our contribution to this endeavour involves targeting the problem of adherence prediction for a particularly challenging class of patients who self-administer injectable medication at home. Our prediction pipeline, based on event history, comprises a connected sharps bin which aims to help patients better manage their condition and improve outcomes. In other words, the efficiency of interventions can be significantly improved by prioritizing the patients who are most likely to be non-adherent. The collected data comprising a rich event feature set may be exploited for the purposes of predicting the status of the next adherence state for individual patients. This paper reports on how this concept can be realized through an investigation using a wide range of ensemble learning and deep learning models on a real-world dataset collected from such a system. The dataset investigated comprises 342,174 historic injection disposal records collected over the course of more than 5 years. A comprehensive comparison of different models is given in this paper. Moreover, we demonstrate that the selected best performer, long short-term memory (LSTM), generalizes well by deploying it in a true future testing dataset. The proposed end-to-end pipeline is capable of predicting patient failure in adhering to their therapeutic regimen with 77.35 % accuracy (Specificity: 78.28 %, Sensitivity: 76.42%, Precision: 77.87%,F1 score: 0.7714, ROC AUC: 0.8390).
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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.