Estimation of Heart Failure Patients Medication Adherence through the Utilization of Saliva and Breath Biomarkers and Data Mining Techniques

Tripoliti, Evanthia E.; Papadopoulos, Theofilos; Karanasiou, Georgia S.; Kalatzis, Fanis G.; Goletsis, Yorgos; Bechlioulis, Aris; Ghimenti, Silvia; Lomonaco, T; Bellagambi, Francesca G.; Fuoco, Roger; Marzilli, Mario; Scali, Maria Chiara; Naka, Katerina K.; Errachid, Abdelhamid; Fotiadis, Dimitrios I.

doi:10.1109/cbms.2017.68

Cited by 3 publications

(3 citation statements)

References 35 publications

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“…The next group had 2 articles that predicted and monitored medication adherence but did not introduce any medication adherence intervention [ 46 , 47 ].…”

Section: Resultsmentioning

confidence: 99%

“…The first of these articles used data collected during hospital stays to generate predictors. Of these predictors, disease severity and biomarkers (breath, saliva, blood) had the largest impact on the model’s accuracy [ 46 ]. The overall goals of this study were to predict the adherence risk of a given patient with heart failure and to monitor their ongoing adherence.…”

Section: Resultsmentioning

confidence: 99%

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Machine Learning and Medication Adherence: Scoping Review

Bohlmann¹,

Mostafa²,

Kumar³

2021

JMIRx Med

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Background This is the first scoping review to focus broadly on the topics of machine learning and medication adherence. Objective This review aims to categorize, summarize, and analyze literature focused on using machine learning for actions related to medication adherence. Methods PubMed, Scopus, ACM Digital Library, IEEE, and Web of Science were searched to find works that meet the inclusion criteria. After full-text review, 43 works were included in the final analysis. Information of interest was systematically charted before inclusion in the final draft. Studies were placed into natural categories for additional analysis dependent upon the combination of actions related to medication adherence. The protocol for this scoping review was created using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Results Publications focused on predicting medication adherence have uncovered 20 strong predictors that were significant in two or more studies. A total of 13 studies that predicted medication adherence used either self-reported questionnaires or pharmacy claims data to determine medication adherence status. In addition, 13 studies that predicted medication adherence did so using either logistic regression, artificial neural networks, random forest, or support vector machines. Of the 15 studies that predicted medication adherence, 6 reported predictor accuracy, the lowest of which was 77.6%. Of 13 monitoring systems, 12 determined medication administration using medication container sensors or sensors in consumer electronics, like smartwatches or smartphones. A total of 11 monitoring systems used logistic regression, artificial neural networks, support vector machines, or random forest algorithms to determine medication administration. The 4 systems that monitored inhaler administration reported a classification accuracy of 93.75% or higher. The 2 systems that monitored medication status in patients with Parkinson disease reported a classification accuracy of 78% or higher. A total of 3 studies monitored medication administration using only smartwatch sensors and reported a classification accuracy of 78.6% or higher. Two systems that provided context-aware medication reminders helped patients to achieve an adherence level of 92% or higher. Two conversational artificial intelligence reminder systems significantly improved adherence rates when compared against traditional reminder systems. Conclusions Creation of systems that accurately predict medication adherence across multiple data sets may be possible due to predictors remaining strong across multiple studies. Higher quality measures of adherence should be adopted when possible so that prediction algorithms are based on accurate information. Currently, medication adherence can be predicted with a good level of accuracy, potentially allowing for the development of interventions aimed at preventing nonadherence. Monitoring systems that track inhaler use currently classify inhaler-related actions with an excellent level of accuracy, allowing for tracking of adherence and potentially proper inhaler technique. Systems that monitor medication states in patients with Parkinson disease can currently achieve a good level of classification accuracy and have the potential to inform medication therapy changes in the future. Medication administration monitoring systems that only use motion sensors in smartwatches can currently achieve a good level of classification accuracy but only when differentiating between a small number of possible activities. Context-aware reminder systems can help patients achieve high levels of medication adherence but are also intrusive, which may not be acceptable to users. Conversational artificial intelligence reminder systems can significantly improve adherence.

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“…The next group had 2 articles that predicted and monitored medication adherence but did not introduce any medication adherence intervention [ 46 , 47 ].…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Machine Learning and Medication Adherence: Scoping Review

Bohlmann¹,

Mostafa²,

Kumar³

2021

JMIRx Med

View full text Add to dashboard Cite

show abstract

“…Non-adherence of HF patients to treatment suggestions (suggestions regarding medication, nutrition and physical activity exercising) has been proven a significant contributor to the presence of HF adverse events [57][58][59][60][61]. In order the effects of non-adherence to be avoided and the management of HF patients to be optimized, the Treatment adherence module evaluates if the patient is adherent or not to the suggestions provided by the experts.…”

Section: Treatment Adherence Modulementioning

confidence: 99%

HEARTEN KMS – A knowledge management system targeting the management of patients with heart failure

Tripoliti

Karanasiou

Kalatzis

et al. 2019

Journal of Biomedical Informatics

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The aim of this work is to present the HEARTEN Knowledge Management System, one of the core modules of the HEARTEN platform. The HEARTEN platform is an mHealth collaborative environment enabling the Heart Failure patients to self-manage the disease and remain adherent, while allowing the other ecosystem actors (healthcare professionals, caregivers, nutritionists, physical activity experts, psychologists) to monitor the patient's health progress and offer personalized, predictive and preventive disease management. The HEARTEN Knowledge Management System is a tool which provides multiple functionalities to the ecosystem actors for the assessment of the patient's condition, the estimation of the patient's adherence, the prediction of potential adverse events, the calculation of Heart Failure related scores, the extraction of statistics, the association of patient clinical and non-clinical data and the provision of alerts and suggestions. The innovation of this tool lays in the analysis of multiparametric personal data coming from different sources, including for the first time breath and saliva biomarkers, and the use of machine learning techniques. The HEARTEN Knowledge Management System consists of nine modules. The accuracy of the KMS modules ranges from 78-95% depending on the module/functionality.

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Machine Learning and Medication Adherence: Scoping Review (Preprint)

Bohlmann¹,

Mostafa²

2021

Preprint

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BACKGROUND This is the first scoping review broadly focused on machine learning and medication adherence. OBJECTIVE To categorize and summarize literature focused on using machine learning for medication compliance activities. METHODS PubMed, Scopus, ACM Digital library, IEEE, and Web of Science were searched to find works that meet the inclusion criteria. Study information of interest was systematically charted before inclusion in the final draft. Studies were placed into natural categories for additional analysis dependent upon the combination of medication adherence activities carried out. The protocol for this scoping review was created using the PRISMA-ScR guidelines. RESULTS Publications focused on predicting medication adherence have uncovered strong predictors that were significant across multiple studies. Studies that used machine learning to monitor medication compliance are generally still in early developmental stages and used a variety of sensor data to detect medication administration. Systems that combined medication monitoring with intervention were mostly concerned with detecting medication administration and only a few compared their system against more traditional approaches. CONCLUSIONS In general, this topic currently has relatively few publications but has been generating more interest over the last few years. Although important features for predicting adherence have been identified more work needs to be done to understand the complex interplay between these features. Systems used to monitor medication compliance also require further testing in more realistic environments and user acceptability evaluations. When interventions are attempted the effectiveness of the system should be evaluated against current systems used to encourage medication compliance. CLINICALTRIAL NONE

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Estimation of Heart Failure Patients Medication Adherence through the Utilization of Saliva and Breath Biomarkers and Data Mining Techniques

Cited by 3 publications

References 35 publications

Machine Learning and Medication Adherence: Scoping Review

Machine Learning and Medication Adherence: Scoping Review

HEARTEN KMS – A knowledge management system targeting the management of patients with heart failure

Machine Learning and Medication Adherence: Scoping Review (Preprint)

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