Machine learning approaches to medication adherence amongst NCD patients: A systematic literature review
Non-adherence to prescribed medication is a major public health concern that escalates the risk of morbidity and death as well as incurring extra expenses associated with hospitalisation. According to the World Health Organization (WHO), only 50% of people suffering from chronic diseases follow the...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Informatics in Medicine Unlocked |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914823000527 |
| _version_ | 1797845084102721536 |
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| author | Wellington Kanyongo Absalom E. Ezugwu |
| author_facet | Wellington Kanyongo Absalom E. Ezugwu |
| author_sort | Wellington Kanyongo |
| collection | DOAJ |
| description | Non-adherence to prescribed medication is a major public health concern that escalates the risk of morbidity and death as well as incurring extra expenses associated with hospitalisation. According to the World Health Organization (WHO), only 50% of people suffering from chronic diseases follow the treatment recommendations despite the counsel provided to patients on the importance of medication adherence (MA). Early detection of non-communicable disease (NCD) patients poorly adhering to recommended medications using analytics based on machine learning (ML) may improve the outcomes of NCD patients positively. This paper presents a systematic review of literature involving the application of ML in evaluating MA amongst NCD patients. The articles considered in this study were extracted from Web of Science, Google Scholar, PubMed, and IEEE Explore. Twenty-five articles in total met the criteria for inclusion. These were articles that utilised ML techniques to analyse MA in NCDs, with patients suffering from diabetes (n = 8), hypertension (n = 3), cardiovascular disease (CVD) and statin adherence (n = 6), cancer (n = 3), respiratory diseases (n = 2), and other NCD conditions (n = 3). The proportion of days covered (PDC) was typically used to evaluate MA. It emerged from the study that for MA to be considered high, the adherence threshold should be at least 75% of the PDC, a universally accepted threshold. In MA analytics research and practice, a PDC ≥80% threshold is typically regarded as a high level of adherence to prescription medication. Logistic regression (LR) (n = 12), random forest (RF) (n = 11), support vector machine (SVM) (n = 7), neural net (n = 6), ensemble learning (n = 6), MLPs (n = 4), XGBoost (n = 3), Bayesian network (BN) (n = 3), and gradient boosting (n = 3) were the most frequently applied ML techniques in the analytics of MA amongst NCD patients. It should be underscored that leveraging standard ML, deep learning (DL), and ensemble learning has enormous potential for measuring MA amongst NCD patients based on various analytics such as prediction, regression, classification, and clustering. Moreover, a further study could be conducted to comprehend how the application of alternative ML-based techniques can be used to measure MA among patients with chronic infectious diseases. |
| first_indexed | 2024-04-09T17:32:47Z |
| format | Article |
| id | doaj.art-b1d76dfe8e6240559388f9da1d5520c7 |
| institution | Directory Open Access Journal |
| issn | 2352-9148 |
| language | English |
| last_indexed | 2024-04-09T17:32:47Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj.art-b1d76dfe8e6240559388f9da1d5520c72023-04-18T04:08:55ZengElsevierInformatics in Medicine Unlocked2352-91482023-01-0138101210Machine learning approaches to medication adherence amongst NCD patients: A systematic literature reviewWellington Kanyongo0Absalom E. Ezugwu1Department of Computer Science, Faculty of Science Engineering, Bindura University of Science Education, Bindura, Zimbabwe; School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg Campus, Pietermaritzburg, 3201, South Africa; Corresponding author. Department of Computer Science, Faculty of Science Engineering, Bindura University of Science Education, Bindura, Zimbabwe.Unit for Data Science and Computing, North-West University, 11 Hoffman Street, Potchefstroom, 2520, South Africa; Corresponding author.Non-adherence to prescribed medication is a major public health concern that escalates the risk of morbidity and death as well as incurring extra expenses associated with hospitalisation. According to the World Health Organization (WHO), only 50% of people suffering from chronic diseases follow the treatment recommendations despite the counsel provided to patients on the importance of medication adherence (MA). Early detection of non-communicable disease (NCD) patients poorly adhering to recommended medications using analytics based on machine learning (ML) may improve the outcomes of NCD patients positively. This paper presents a systematic review of literature involving the application of ML in evaluating MA amongst NCD patients. The articles considered in this study were extracted from Web of Science, Google Scholar, PubMed, and IEEE Explore. Twenty-five articles in total met the criteria for inclusion. These were articles that utilised ML techniques to analyse MA in NCDs, with patients suffering from diabetes (n = 8), hypertension (n = 3), cardiovascular disease (CVD) and statin adherence (n = 6), cancer (n = 3), respiratory diseases (n = 2), and other NCD conditions (n = 3). The proportion of days covered (PDC) was typically used to evaluate MA. It emerged from the study that for MA to be considered high, the adherence threshold should be at least 75% of the PDC, a universally accepted threshold. In MA analytics research and practice, a PDC ≥80% threshold is typically regarded as a high level of adherence to prescription medication. Logistic regression (LR) (n = 12), random forest (RF) (n = 11), support vector machine (SVM) (n = 7), neural net (n = 6), ensemble learning (n = 6), MLPs (n = 4), XGBoost (n = 3), Bayesian network (BN) (n = 3), and gradient boosting (n = 3) were the most frequently applied ML techniques in the analytics of MA amongst NCD patients. It should be underscored that leveraging standard ML, deep learning (DL), and ensemble learning has enormous potential for measuring MA amongst NCD patients based on various analytics such as prediction, regression, classification, and clustering. Moreover, a further study could be conducted to comprehend how the application of alternative ML-based techniques can be used to measure MA among patients with chronic infectious diseases.http://www.sciencedirect.com/science/article/pii/S2352914823000527Medication adherenceMachine learningDeep learningNon-communicable diseaseChronic diseaseNCD patients |
| spellingShingle | Wellington Kanyongo Absalom E. Ezugwu Machine learning approaches to medication adherence amongst NCD patients: A systematic literature review Informatics in Medicine Unlocked Medication adherence Machine learning Deep learning Non-communicable disease Chronic disease NCD patients |
| title | Machine learning approaches to medication adherence amongst NCD patients: A systematic literature review |
| title_full | Machine learning approaches to medication adherence amongst NCD patients: A systematic literature review |
| title_fullStr | Machine learning approaches to medication adherence amongst NCD patients: A systematic literature review |
| title_full_unstemmed | Machine learning approaches to medication adherence amongst NCD patients: A systematic literature review |
| title_short | Machine learning approaches to medication adherence amongst NCD patients: A systematic literature review |
| title_sort | machine learning approaches to medication adherence amongst ncd patients a systematic literature review |
| topic | Medication adherence Machine learning Deep learning Non-communicable disease Chronic disease NCD patients |
| url | http://www.sciencedirect.com/science/article/pii/S2352914823000527 |
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