Computers in biology and medicine · 2025
Methods and computational techniques for predicting adherence to treatment: A scoping review.
СтатьяScoping ReviewНазвание и аннотация: Перевод пока не подготовлен
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Merino-Barbancho Beatriz, Cipric Ana, Arroyo Peña, Rujas Miguel, Gómez Del Moral Herranz Rodrigo Martín, Barev Torben, Ciccone Nicholas, Fico Giuseppe
Аннотация
BACKGROUND: Treatment non-adherence of patients stands as a major barrier to effectively manage chronic conditions. However, non-adherent behavior is estimated to affect up to 50 % of patients with chronic conditions, leading to poorer health outcomes among patients, higher rates of hospitalization, and increased mortality.
OBJECTIVE: This study offers a provision of a structured overview of the computational methods and techniques used to build predictive models of treatment adherence of patients.
METHODS: A scoping review was conducted, and the following databases were searched to identify relevant publications: PubMed, IEEE and Web of Science. The screening of publications consisted of two steps. First, the hits obtained from the search were independently screened and selected using an open-source machine learning (ML)-aided pipeline applying active learning: ASReview, Active learning for Systematic Reviews. Publications selected for full-text review and data extraction were those highly prioritized by ASReview.
RESULTS: A total of 45 papers were selected into the second round of full-text screening and 29 papers were considered in the final review. The findings suggest supervised learning (regression and classification) to be the most used analytical approach, being the generalized linear models (GEE) (21.67 %), logistic regressions (20 %) and random forest (18.33 %) the most frequently employed techniques. The family of GEE identified in the studies included some multiple, hierarchical or mixed-effect models, among other. The selection of these models often depended on data source and types (e.g., logistic regressions for dichotomous outcome measures). Furthermore, over 54 % of adherence topics being related to chronic metabolic conditions such as diabetes, hypertension, and hyperlipidemia. Most assessed predictors were both treatment and socio-demographic and economic-related factors followed by condition-related factors. The adherence to treatment variable was mostly dichotomous (12 out of 29) and computed using metrics as the Medical Possession Ratio with a 80 % threshold. A limitation of the reviewed studies is the lack of accountancy for interrelationships between different determinants of adherence behavior, denoting the need for future research regarding the use of more complex analytical techniques that better capture these connections (e.g., patient's socio-economic status and the ability to afford medication).
CONCLUSION: The creation of systems to accurately predict treatment adherence can pave the way for improved therapeutic outcomes, reduced healthcare costs and enabling personalized treatment plans. This paper can support to understand the efforts made in the field of modeling adherence-related factors. In particular, the results provide a structured overview of the computational methods and techniques used to build predictive models of treatment adherence of patients in order to guide future advancements in healthcare.
OBJECTIVE: This study offers a provision of a structured overview of the computational methods and techniques used to build predictive models of treatment adherence of patients.
METHODS: A scoping review was conducted, and the following databases were searched to identify relevant publications: PubMed, IEEE and Web of Science. The screening of publications consisted of two steps. First, the hits obtained from the search were independently screened and selected using an open-source machine learning (ML)-aided pipeline applying active learning: ASReview, Active learning for Systematic Reviews. Publications selected for full-text review and data extraction were those highly prioritized by ASReview.
RESULTS: A total of 45 papers were selected into the second round of full-text screening and 29 papers were considered in the final review. The findings suggest supervised learning (regression and classification) to be the most used analytical approach, being the generalized linear models (GEE) (21.67 %), logistic regressions (20 %) and random forest (18.33 %) the most frequently employed techniques. The family of GEE identified in the studies included some multiple, hierarchical or mixed-effect models, among other. The selection of these models often depended on data source and types (e.g., logistic regressions for dichotomous outcome measures). Furthermore, over 54 % of adherence topics being related to chronic metabolic conditions such as diabetes, hypertension, and hyperlipidemia. Most assessed predictors were both treatment and socio-demographic and economic-related factors followed by condition-related factors. The adherence to treatment variable was mostly dichotomous (12 out of 29) and computed using metrics as the Medical Possession Ratio with a 80 % threshold. A limitation of the reviewed studies is the lack of accountancy for interrelationships between different determinants of adherence behavior, denoting the need for future research regarding the use of more complex analytical techniques that better capture these connections (e.g., patient's socio-economic status and the ability to afford medication).
CONCLUSION: The creation of systems to accurately predict treatment adherence can pave the way for improved therapeutic outcomes, reduced healthcare costs and enabling personalized treatment plans. This paper can support to understand the efforts made in the field of modeling adherence-related factors. In particular, the results provide a structured overview of the computational methods and techniques used to build predictive models of treatment adherence of patients in order to guide future advancements in healthcare.