Use of machine learning to predict medication adherence in individuals at risk for atherosclerotic cardiovascular disease

Mirzadeh, Seyed Iman; Arefeen, Asiful; Ardo, Jessica; Fallahzadeh, Ramin; Minor, Bryan; Lee, Jung-Ah; Hildebrand, Janett A.; Cook, Diane J.; Ghasemzadeh, Hassan; Evangelista, Lorraine S.

doi:10.1016/j.smhl.2022.100328

Cited by 4 publications

(2 citation statements)

References 69 publications

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“…Compared to traditional statistical methods, machine learning provides numerous advantages such as increased flexibility, prediction accuracy, possibility of automation, and processing of big data. Prediction models for adherence have already been developed and tested in various scenarios [19][20][21][22]. If adequately reported, these models can help guide treatment decision-making, improve patient outcomes, and streamline perioperative health care management.…”

Section: Risk-predictive Models and Decision Support Systemsmentioning

confidence: 99%

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

Masiero,

Spada,

Sanchini

et al. 2023

JMIR Res Protoc

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Background Adherence to oral anticancer treatments is critical in the disease trajectory of patients with breast cancer. Given the impact of nonadherence on clinical outcomes and the associated economic burden for the health care system, finding ways to increase treatment adherence is particularly relevant. Objective The primary end point is to evaluate the effectiveness of a decision support system (DSS) and a machine learning web application in promoting adherence to oral anticancer treatments among patients with metastatic breast cancer. The secondary end point is to collect a set of new physical, psychological, social, behavioral, and quality of life predictive variables that could be used to refine the preliminary version of the machine learning model to predict patients’ adherence behavior. Methods This prospective, randomized controlled study is nested in a large-scale international project named “Enhancing therapy adherence among metastatic breast cancer patients” (Pfizer 65080791), aimed to develop a predictive model of nonadherence and associated DSS and guidelines to foster patients’ engagement and therapy adherence. A web-based DSS named TREAT (treatment adherence support) was developed using a patient-driven approach, with 4 sections, that is, Section A: Metastatic Breast Cancer; Section B: Adherence to Cancer Therapies; Section C: Promoting Adherence; and Section D: My Adherence Diary. Moreover, a machine learning–based web application was developed to predict patients' risk factors of adherence to anticancer treatment, specifically pertaining to physical status and comorbid conditions, as well as short and long-term side effects. Overall, 100 patients consecutively admitted at the European Institute of Oncology (IEO) at the Division of Medical Senology will be enrolled; 50 patients with metastatic breast cancer will be exposed to the DSS and machine learning web application for 3 months (experimental group), and 50 patients will not be exposed to the intervention (control group). Each participant will fill a weekly medication diary and a set of standardized self-reports evaluating psychological and quality of life variables (Adherence Attitude Inventory, Beck Depression Inventory-II, Brief Pain Inventory, 13-item Sense of Coherence scale, Brief Italian version of Cancer Behavior Inventory, European Organization for Research and Treatment of Cancer Quality of Life 23-item Breast Cancer-specific Questionnaire, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, 8-item Morisky Medication Adherence Scale, State-Trait Anxiety Inventory forms I and II, Big Five Inventory, and visual analogue scales evaluating risk perception). The 3 assessment time points are T0 (baseline), T1 (1 month), T2 (2 months), and T3 (3 months). This study was approved by the IEO ethics committee (R1786/22-IEO 1907). Results The recruitment process started in May 2023 and is expected to conclude on December 2023. Conclusions The contribution of machine learning techniques through risk-predictive models integrated into DSS will enable medication adherence by patients with cancer. Trial Registration ClinicalTrials.gov NCT06161181; https://clinicaltrials.gov/study/NCT06161181 International Registered Report Identifier (IRRID) DERR1-10.2196/48852

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Section: Risk-predictive Models and Decision Support Systemsmentioning

confidence: 99%

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

Masiero,

Spada,

Sanchini

et al. 2023

JMIR Res Protoc

View full text Add to dashboard Cite

show abstract

“…Both methods have shown excellent performance. The deployment of machine-learning algorithms in the prediction of adherence in cardiovascular disease including random forests, support vector machines, and neural networks showed the accuracy ranged from 0.53 to 0.97 ( Mirzadeh et al, 2022 ; Zakeri et al, 2022 ). The ensemble learning model in the prediction of adherence from the patients who conducted self-administer injections proposed by Gu et al (2021) achieved a good performance and generalization properties based on the fusion of multiple heterogeneous classifiers.…”

Section: Discussionmentioning

confidence: 99%

Sequential model for predicting patient adherence in subcutaneous immunotherapy for allergic rhinitis

Li,

Xiong,

Fan

et al. 2024

Front. Pharmacol.

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ObjectiveSubcutaneous Immunotherapy (SCIT) is the long-lasting causal treatment of allergic rhinitis (AR). How to enhance the adherence of patients to maximize the benefit of allergen immunotherapy (AIT) plays a crucial role in the management of AIT. This study aims to leverage novel machine learning models to precisely predict the risk of non-adherence of AR patients and related local symptom scores in 3 years SCIT.MethodsThe research develops and analyzes two models, sequential latent-variable model (SLVM) of Stochastic Latent Actor-Critic (SLAC) and Long Short-Term Memory (LSTM). SLVM is a probabilistic model that captures the dynamics of patient adherence, while LSTM is a type of recurrent neural network designed to handle time-series data by maintaining long-term dependencies. These models were evaluated based on scoring and adherence prediction capabilities.ResultsExcluding the biased samples at the first time step, the predictive adherence accuracy of the SLAC models is from 60% to 72%, and for LSTM models, it is 66%–84%, varying according to the time steps. The range of Root Mean Square Error (RMSE) for SLAC models is between 0.93 and 2.22, while for LSTM models it is between 1.09 and 1.77. Notably, these RMSEs are significantly lower than the random prediction error of 4.55.ConclusionWe creatively apply sequential models in the long-term management of SCIT with promising accuracy in the prediction of SCIT nonadherence in AR patients. While LSTM outperforms SLAC in adherence prediction, SLAC excels in score prediction for patients undergoing SCIT for AR. The state-action-based SLAC adds flexibility, presenting a novel and effective approach for managing long-term AIT.

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Feature selection and importance of predictors of non-communicable diseases medication adherence from machine learning research perspectives

Kanyongo

Ezugwu

2023

Informatics in Medicine Unlocked

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Use of machine learning to predict medication adherence in individuals at risk for atherosclerotic cardiovascular disease

Cited by 4 publications

References 69 publications

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

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

Sequential model for predicting patient adherence in subcutaneous immunotherapy for allergic rhinitis

Feature selection and importance of predictors of non-communicable diseases medication adherence from machine learning research perspectives

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