Methods and computational techniques for predicting adherence to treatment: A scoping review
Beatriz Merino-Barbancho1, Ana Cipric2, Peña Arroyo1
1Universidad Politécnica de Madrid, Life Supporting Technologies Research Group, Madrid, Spain.
Computers in Biology and Medicine
|April 27, 2025
Summary
Predictive models for patient treatment adherence are crucial for managing chronic conditions. This review highlights supervised learning techniques like generalized linear models and logistic regressions as key computational methods for building these models.
Area of Science:
- Computational methods in healthcare
- Machine learning applications in medicine
- Predictive modeling for patient adherence
Background:
- Treatment non-adherence affects up to 50% of patients with chronic conditions.
- Non-adherence leads to poorer health outcomes, increased hospitalizations, and mortality.
- Effective management of chronic conditions is hindered by patient non-adherence.
Purpose of the Study:
- To provide a structured overview of computational methods for predictive modeling of patient treatment adherence.
- To identify and categorize techniques used in building adherence prediction models.
- To guide future research and advancements in healthcare modeling.
Main Methods:
- A scoping review was conducted.
- Databases searched include PubMed, IEEE, and Web of Science.
- Machine learning-aided pipeline (ASReview) with active learning was used for initial screening.
Main Results:
- Supervised learning (regression, classification) is the most common approach.
- Generalized linear models (21.67%), logistic regressions (20%), and random forest (18.33%) are frequently used.
- Over 54% of adherence topics relate to chronic metabolic conditions; predictors include treatment, socio-demographic, and condition-related factors.
Conclusions:
- Accurate prediction of treatment adherence can improve outcomes and reduce costs.
- This overview supports understanding of adherence modeling efforts.
- Results guide future advancements in computational methods for personalized treatment plans.
Related Concept Videos
02:07Blind Procedures
10.6K
Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...
10.6K
01:52Regression Toward the Mean
6.2K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.2K
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
106
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
106
01:25Strategies for Assessing and Addressing Confounding
63
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
63
01:20Comparing the Survival Analysis of Two or More Groups
84
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
84
01:28Drug Therapy
28
The advent of drug therapy has profoundly shaped modern mental health care, providing targeted treatments for a range of psychological disorders. Psychotherapeutic drugs, classified into antianxiety, antidepressant, and antipsychotic medications, address symptoms across anxiety disorders, mood disorders, and schizophrenia. While these medications have transformed patient outcomes, they require careful management due to their potential side effects and limitations.
Antianxiety Medications
Antianxiety Medications
28