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AI for personalized medicine

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dc.contributor.authorLewis, Aidan Michael, author
dc.contributor.authorBailey, Susan M., advisor
dc.contributor.authorLaRocca, Tom J., committee member
dc.contributor.authorNishimura, Erin O., committee member
dc.date.accessioned2023-08-28T10:27:52Z
dc.date.available2024-08-28T10:27:54Z
dc.date.issued2023
dc.description.abstractIn 2021, Americans spent an estimated $4.3 trillion dollars on healthcare, an extraordinary amount for treatment that is often less effective than care in other developed nations (1-3). Precision, or personalized, medicine represents a new frontier in healthcare that promises treatment plans and optimized health strategies tailored to an individual (4) thereby making medicine more effective and less costly. Contemporary Artificial Intelligence (AI) and Machine Learning (ML) approaches have tremendous potential to advance the field of precision medicine by leveraging the technology's power of deciphering patterns in the data to make predictions about an individual's health outcomes (3, 5-8). However, many developing AI/ML approaches to precision medicine have not proven particularly successful in making accurate predictions and conclusions mostly due to the limited availability of high-quality medical data for input. The Wake Forrest University Non-Human Primate Radiation Late Effects Cohort (NHP RLEC) provides an unprecedented opportunity to test AI's ability to be trained on a comprehensive human health analog data set in an experimentally irradiated Rhesus monkey cohort with extraordinary life-time records of biomarker levels and health outcomes. Here, we test prevalent, scalable ML models to improve accuracy of predictions specifically related to radiation biomarkers, dose, and health outcomes. We find that CatBoost, ElasticNet, and XGBoost models can accurately predict lymphocyte counts for both monkey populations and individual monkeys. Furthermore, these models can accurately predict radiation dose and biomarker levels using only five other features within the models. Although the models were only marginally successful at predicting lymphocyte counts using radiation-related data alone, and at predicting the health outcomes of the monkeys, these findings and this unique dataset represent key steps toward refining the combinations of factors necessary for the successful use of AI/ML models in precision medicine for humans.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierLewis_colostate_0053N_17889.pdf
dc.identifier.urihttps://hdl.handle.net/10217/236818
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.rights.accessEmbargo expires: 08/28/2024.
dc.subjectartificial intelligence
dc.subjectmedicine
dc.subjectspace
dc.subjectastronautics
dc.subjectAI
dc.subjectradiation
dc.titleAI for personalized medicine
dc.typeText
dcterms.embargo.expires2024-08-28
dcterms.embargo.terms2024-08-28
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineEnvironmental and Radiological Health Sciences
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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