AIMC Topic: Medication Adherence
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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
Jul 6, 2022
BACKGROUND: Our aim was to develop a machine learning model, using real-world data captured from a connected auto-injector device and from early indicators from the first 3 months of treatment, to predict sub-optimal adherence to recombinant human gr...
A machine-learning assisted review of the use of habit formation in medication adherence interventions for long-term conditions.
Health psychology review
Feb 14, 2022
Adherence to medication in long-term conditions is around 50%. The key components of successful interventions to improve medication adherence remain unclear, particularly when examined over prolonged follow-up periods. Behaviour change theories are i...
Machine learning models to identify low adherence to influenza vaccination among Korean adults with cardiovascular disease.
BMC cardiovascular disorders
Mar 9, 2021
BACKGROUND: Annual influenza vaccination is an important public health measure to prevent influenza infections and is strongly recommended for cardiovascular disease (CVD) patients, especially in the current coronavirus disease 2019 (COVID-19) pandem...
Accuracy of machine learning-based prediction of medication adherence in clinical research.
Psychiatry research
Nov 4, 2020
Medication non-adherence represents a significant barrier to treatment efficacy. Remote, real-time measurement of medication dosing can facilitate dynamic prediction of risk for medication non-adherence, which in-turn allows for proactive clinical in...
Deep CNN Sparse Coding for Real Time Inhaler Sounds Classification.
Sensors (Basel, Switzerland)
Apr 21, 2020
Effective management of chronic constrictive pulmonary conditions lies in proper and timely administration of medication. As a series of studies indicates, medication adherence can effectively be monitored by successfully identifying actions performe...
Random forest machine learning algorithm predicts virologic outcomes among HIV infected adults in Lausanne, Switzerland using electronically monitored combined antiretroviral treatment adherence.
AIDS care
Apr 8, 2020
Machine Learning (ML) can improve the analysis of complex and interrelated factors that place adherent people at risk of viral rebound. Our aim was to build ML model to predict RNA viral rebound from medication adherence and clinical data. Patients w...
A Scalable Smartwatch-Based Medication Intake Detection System Using Distributed Machine Learning.
Journal of medical systems
Feb 28, 2020
Poor Medication adherence causes significant economic impact resulting in hospital readmission, hospital visits and other healthcare costs. The authors developed a smartwatch application and a cloud based data pipeline for developing a user-friendly ...
Predictors of adherence to nicotine replacement therapy: Machine learning evidence that perceived need predicts medication use.
Drug and alcohol dependence
Oct 25, 2019
BACKGROUND: Nonadherence to smoking cessation medication is a frequent problem. Identifying pre-quit predictors of nonadherence may help explain nonadherence and suggest tailored interventions to address it.
What do patients learn about psychotropic medications on the web? A natural language processing study.
Journal of affective disorders
Sep 10, 2019
BACKGROUND: Low rates of medication adherence remain a major challenge across psychiatry. In part, this likely reflects patient concerns about safety and adverse effects, accurate or otherwise. We therefore sought to characterize online information a...
Perceptions of drones, digital adherence monitoring technologies and educational videos for tuberculosis control in remote Madagascar: a mixed-method study protocol.
BMJ open
May 9, 2019
INTRODUCTION: Poor road and communication infrastructure pose major challenges to tuberculosis (TB) control in many regions of the world. TB surveillance and patient support often fall to community health workers (CHWs) who may lack the time or knowl...
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