Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/248173
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dc.titleHARNESSING NEURAL NETWORK DISCOVERED CORRELATIONS IN MEDICINE TO OPTIMIZE PATIENT TREATMENT
dc.contributor.authorCHONG LI MING
dc.date.accessioned2024-04-30T18:01:19Z
dc.date.available2024-04-30T18:01:19Z
dc.date.issued2023-11-29
dc.identifier.citationCHONG LI MING (2023-11-29). HARNESSING NEURAL NETWORK DISCOVERED CORRELATIONS IN MEDICINE TO OPTIMIZE PATIENT TREATMENT. ScholarBank@NUS Repository.
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/248173
dc.description.abstractWithout requiring knowledge of pre-existing population-driven data, phenotypic medicine platforms (PPMs) such as CURATE.AI were employed. These recommendations were based on predictions provided by the patient’s own treatment profile longitudinally. First, a prospective feasibility trial has been conducted to guide and modulate Ibrutinib doses for Waldenström’s macroglobulinemia with CURATEAI. Using the methodology of this study as a basis, a perspective piece is provided on how PPMs can be integrated with radiation oncology, and the possible implementation of a CURATE.AI-guided radiotherapy. Lastly, the aims, hypotheses, and methods of an in vitro CURATE.AI-guided radiation therapy study are provided. In this thesis, PPMs such as CURATE.AI have demonstrated their feasibility and potential in personalizing treatment for oncology.
dc.language.isoen
dc.subjectN-of-1,oncology,clinical trial,ai-derived,personalized medicine,digital medicine
dc.typeThesis
dc.contributor.departmentBIOMEDICAL ENGINEERING
dc.contributor.supervisorDean Ho
dc.contributor.supervisorAnqi Qiu
dc.contributor.supervisorChen Hua Yeow
dc.description.degreePh.D
dc.description.degreeconferredDOCTOR OF PHILOSOPHY (CDE-ENG)
dc.identifier.orcid0000-0002-3742-2704
Appears in Collections:Ph.D Theses (Open)

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