Please use this identifier to cite or link to this item: https://doi.org/10.3390/ijerph19158979
Title: Personalised Dosing Using the CURATE.AI Algorithm: Protocol for a Feasibility Study in Patients with Hypertension and Type II Diabetes Mellitus
Authors: Mukhopadhyay, Amartya 
Sumner, Jennifer 
Ling, Lieng Hsi 
Quek, Raphael Hao Chong 
Tan, Andre Teck Huat 
Teng, Gim Gee 
Seetharaman, Santhosh Kumar 
Gollamudi, Satya Pavan Kumar 
Ho, Dean 
Motani, Mehul 
Keywords: chronic disease management
ambulatory care
self-management
artificial intelligence
personalised medicine
Issue Date: 1-Aug-2022
Publisher: MDPI
Citation: Mukhopadhyay, Amartya, Sumner, Jennifer, Ling, Lieng Hsi, Quek, Raphael Hao Chong, Tan, Andre Teck Huat, Teng, Gim Gee, Seetharaman, Santhosh Kumar, Gollamudi, Satya Pavan Kumar, Ho, Dean, Motani, Mehul (2022-08-01). Personalised Dosing Using the CURATE.AI Algorithm: Protocol for a Feasibility Study in Patients with Hypertension and Type II Diabetes Mellitus. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 19 (15). ScholarBank@NUS Repository. https://doi.org/10.3390/ijerph19158979
Abstract: 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.
Source Title: INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
URI: https://scholarbank.nus.edu.sg/handle/10635/234857
ISSN: 1661-7827
1660-4601
DOI: 10.3390/ijerph19158979
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