ArticleHealthcare technology letters2016
Predicting adherence of patients with HF through machine learning techniques.
Article in Healthcare technology letters, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06161181 (Enhancing Therapy Adherence Among Metastatic Breast Cancer Patients), which is not on this map. Cited by 30 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Enhancing Therapy Adherence Among Metastatic Breast Cancer Patients: the Study Protocol
Who cites it
30 citing papers in PubMed.
- Predicting adherence of imatinib in gastrointestinal stromal tumor-rise of the machines!Journal of gastrointestinal oncology · 2026Article
- Artificial intelligence-based approaches for monitoring medication adherence among cardiovascular disease patients: a scoping review.BMC medical informatics and decision making · 2026Article
- Machine learning approaches to predicting medication nonadherence: a scoping review.International journal of medical informatics · 2025Article
- Machine learning based prediction of medication adherence in heart failure using large electronic health record cohort with linkages to pharmacy-fill and neighborhood-level data.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Machine learning enhanced expert system for detecting heart failure decompensation using patient reported vitals and electronic health records.Scientific reports · 2025Article
- Development and Validation of Predictive Models for Non-Adherence to Antihypertensive Medication.Medicina (Kaunas, Lithuania) · 2025Article
- A computational framework for longitudinal medication adherence prediction in breast cancer survivors: A social cognitive theory based approach.PLOS digital health · 2025Article
- Biomarkers and Mental Disorders: A Relevance Analysis Using a Random Forest Algorithm.Biomolecules · 2025Article
- Predicting In-Hospital Mortality in Patients With Acute Myocardial Infarction: A Comparison of Machine Learning Approaches.Clinical cardiology · 2025Article
- Machine learning model to predict the adherence of tuberculosis patients experiencing increased levels of liver enzymes in Indonesia.PloS one · 2025Article
- Artificial Intelligence in Heart Failure: Friend or Foe?Life (Basel, Switzerland) · 2024Review
- Opioid Nonadherence Risk Prediction of Patients with Cancer-Related Pain Based on Five Machine Learning Algorithms.Pain research & management · 2024Article
- A Machine Learning Model to Predict Patients' Adherence Behavior and a Decision Support System for Patients With Metastatic Breast Cancer: Protocol for a Randomized Controlled Trial.JMIR research protocols · 2023Article
- Article
- Machine learning-based prediction of 1-year mortality in hypertensive patients undergoing coronary revascularization surgery.Clinical cardiology · 2023Article
- An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review.Biomedicines · 2022Review
- Integrated Digital Health Solutions in the Management of Growth Disorders in Pediatric Patients Receiving Growth Hormone Therapy: A Retrospective Analysis.Frontiers in endocrinology · 2022Article
- Development and assessment of novel machine learning models to predict medication non-adherence risks in type 2 diabetics.Frontiers in public health · 2022Article
- Machine learning algorithms for predicting mortality after coronary artery bypass grafting.Frontiers in cardiovascular medicine · 2022Article
- Machine Learning and Medication Adherence: Scoping Review.JMIRx med · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heart failure (HF) is a chronic disease characterised by poor quality of life, recurrent hospitalisation and high mortality. Adherence of patient to treatment suggested by the experts has been proven a significant deterrent of the above-mentioned serious consequences. However, the non-adherence rates are significantly high; a fact that highlights the importance of predicting the adherence of the patient and enabling experts to adjust accordingly patient monitoring and management. The aim of this work is to predict the adherence of patients with HF, through the application of machine learning techniques. Specifically, it aims to classify a patient not only as medication adherent or not, but also as adherent or not in terms of medication, nutrition and physical activity (global adherent). Two classification problems are addressed: (i) if the patient is global adherent or not and (ii) if the patient is medication adherent or not. About 11 classification algorithms are employed and combined with feature selection and resampling techniques. The classifiers are evaluated on a dataset of 90 patients. The patients are characterised as medication and global adherent, based on clinician estimation. The highest detection accuracy is 82 and 91% for the first and the second classification problem, respectively.
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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.