AIMC Topic: Medication Adherence
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Artificial intelligence-based prediction of treatment failure and medication non-adherence in overactive bladder management.
BMC urology
Aug 21, 2025
BACKGROUND: Overactive bladder management presents significant challenges, with treatment failures and medication non-adherence posing substantial barriers to patient outcomes. Early prediction of these challenges could enable timely interventions an...
Medication adherence among Jordanian adults with chronic conditions: a combined analysis using regression and machine learning.
Annals of medicine
Aug 20, 2025
BACKGROUND: Managing chronic illness effectively depends not only on treatment availability but also on patients' ability to adhere to prescribed medications.
Application of causal forest double machine learning (DML) approach to assess tuberculosis preventive therapy's impact on ART adherence.
Scientific reports
Aug 9, 2025
Adherence to antiretroviral therapy (ART) is critical for HIV treatment success, yet the impact of tuberculosis preventive therapy (TPT) remains inadequately understood. Using observational data from 4152 HIV patients in Ethiopia (2005-2024), we appl...
Predicting antiretroviral therapy adherence status of adult HIV-positive patients using machine-learning Northwest, Ethiopia, 2025.
BMC medical informatics and decision making
Jul 10, 2025
BACKGROUND: Adherence with Anti-Retroviral Therapy (ART) reduces viral load, as well as HIV-related morbidity and mortality. Despite the expanded availability of ART, non-adherence remains a series problem, leads increased viral load, a decline CD4 c...
A longitudinal observational study with ecological momentary assessment and deep learning to predict non-prescribed opioid use, treatment retention, and medication nonadherence among persons receiving medication treatment for opioid use disorder.
Journal of substance use and addiction treatment
Mar 22, 2025
BACKGROUND: Despite effective treatments for opioid use disorder (OUD), relapse and treatment drop-out diminish their efficacy, increasing the risks of adverse outcomes, including death. Predicting important outcomes, including non-prescribed opioid ...
Machine learning applications to classify and monitor medication adherence in patients with type 2 diabetes in Ethiopia.
Frontiers in endocrinology
Mar 20, 2025
BACKGROUND: Medication adherence plays a crucial role in determining the health outcomes of patients, particularly those with chronic conditions like type 2 diabetes. Despite its significance, there is limited evidence regarding the use of machine le...
Using machine learning to predict poor adherence to antiretroviral therapy among adolescents with HIV in low resource settings.
AIDS (London, England)
Feb 24, 2025
OBJECTIVES: Achieving optimal adherence to antiretroviral therapy (ART) and viral suppression is still insufficient for attaining the UNAIDS 95-95-95 target of 2030, especially among adolescents with HIV (AWHIV). This study sought to develop a model ...
Interventions to improve medication adherence in persons with mental disorders.
Current opinion in psychiatry
Jan 30, 2025
PURPOSE OF REVIEW: Nonadherence to medication is prevalent in patients with mental illness. Various factors responsible for it. As a result, there are more hospitalizations, increased risk of suicide and increased cost of care. Thus, there is a need ...
Machine learning model to predict the adherence of tuberculosis patients experiencing increased levels of liver enzymes in Indonesia.
PloS one
Jan 24, 2025
Indonesia is still the second-highest tuberculosis burden country in the world. The antituberculosis adverse drug reaction and adherence may influence the success of treatment. The objective of this study is to define the model for predicting the adh...
Identifying the Relative Importance of Factors Influencing Medication Compliance in General Patients Using Regularized Logistic Regression and LightGBM: Web-Based Survey Analysis.
JMIR formative research
Dec 23, 2024
BACKGROUND: Medication compliance, which refers to the extent to which patients correctly adhere to prescribed regimens, is influenced by various psychological, behavioral, and demographic factors. When analyzing these factors, challenges such as mul...
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