Personalised Dosing Using the CURATE.AI Algorithm: Protocol for a Feasibility Study in Patients with Hypertension and Type II Diabetes Mellitus
Open accessAbstract
Chronic diseases typically require long-term management through healthy lifestyle practices and pharmacological intervention. Although efficacious treatments exist, disease control is often sub-optimal leading to chronic disease-related sequela. Poor disease control can partially be explained by the ‘one size fits all’ pharmacological approach. Precision medicine aims to tailor treatments to the individual. CURATE.AI is a dosing optimisation platform that considers individual factors to improve the precision of drug therapies. CURATE.AI has been validated in other therapeutic areas, such as cancer, but has yet to be applied in chronic disease care. We will evaluate the CURATE.AI system through a single-arm feasibility study (n = 20 hypertensives and n = 20 type II diabetics). Dosing decisions will be based on CURATE.AI recommendations. We will prospectively collect clinical and qualitative data and report on the clinical effect, implementation challenges, and acceptability of using CURATE.AI. In addition, we will explore how to enhance the algorithm further using retrospective patient data. For example, the inclusion of other variables, the simultaneous optimisation of multiple drugs, and the incorporation of other artificial intelligence algorithms. Overall, this project aims to understand the feasibility of using CURATE.AI in clinical practice. Barriers and enablers to CURATE.AI will be identified to inform the system’s future development.
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ML algorithms analyze data from wearable devices such as accelerometers and gyroscopes to monitor motor symptoms and allow healthcare providers to adjust medication dosages.
“They analyze data from wearable devices, such as accelerometers and gyroscopes, to monitor motor symptoms and allow healthcare providers to adjust medication dosages [189,190].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [189]
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- Authors
- Mukhopadhyay, A., Sumner, J., Ling, L. H., Quek, R. H. C., Tan, A. T. H., Teng, G. G., Seetharaman, S. K., Gollamudi, S. P. K., Ho, D., & Motani, M.
- Affiliations
- National University of Singapore (SG)
- National University Hospital (SG)
- Alexandra Hospital (SG)
- Corresponding author
- Sumner, Jennifer
- ORCID
- Venue
- International Journal of Environmental Research and Public Health
- Publisher
- MDPI AG
- Published
- 2022-07-23
- Volume
- 19
- Issue
- 15
- Pages
- 8979
- ISSN
- 1660-4601 (ISSN-L)
- PMC
- PMC9332044
- OpenAlex
- W4287009821
- Cited by
- 38
- References
- 35
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 15 · 2023: 7 · 2022: 1
- FWCI
- 2.41
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Journal of Environmental Research and Public Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- National University of Singapore (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Health Innovation Programme, National University of Singapore (CF/THK/19/13)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- individualized dosing, hypertension, type 2 diabetes, precision medicine, feasibility study, implementation barriers and facilitators, acceptability, chronic disease management
- MeSH terms
- Algorithms, Artificial Intelligence, Chronic Disease, Diabetes Mellitus, Type 2, Feasibility Studies, Humans, Hypertension, Retrospective Studies
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