Personalized Treatment Plans Powered by AI and Genomics
DOI:
https://doi.org/10.32628/CSEIT241039Keywords:
Artificial Intelligence, Genomics, Personalized Medicine, Cancer Treatment, Precision MedicineAbstract
This paper aims at investigating the implementation of AI in genomics specifically in the cancer treatment. Through using the Random Forest and Neural Network models, it would like to find out the prediction of patient outcomes from the genomic and clinical features dataset. One of the acknowledged conclusions of the research is the correlation between the genomic mutations and treatment efficacy, with the affecting genes highlighted to be TP53 and BRCA1. While the result of the models under different configurations show high accuracy, some issues such as data bias and ethical issues including patient privacy concern are still valid in the use of the models. The outcome identifies the need to improve AI models, incorporate different sets of data, and progress the understanding of genomic interpretations for optimizing the clinical applicability of personalized medicine.
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Esplin, E.D., Oei, L. and Snyder, M.P., 2014. Personalized sequencing and the future of medicine: discovery, diagnosis and defeat of disease. Pharmacogenomics, 15(14), pp.1771-1790.
Krzyszczyk, P., Acevedo, A., Davidoff, E.J., Timmins, L.M., Marrero-Berrios, I., Patel, M., White, C., Lowe, C., Sherba, J.J., Hartmanshenn, C. and O’Neill, K.M., 2018. The growing role of precision and personalized medicine for cancer treatment. Technology, 6(03n04), pp.79-100.
Mahadasa, R., 2017. Decoding the Future: Artificial Intelligence in Healthcare. Malaysian Journal of Medical and Biological Research, 4(2), pp.167-174.
Wilson, B.J. and Nicholls, S.G., 2015. The Human Genome Project, and recent advances in personalized genomics. Risk management and healthcare policy, pp.9-20.
Abul-Husn, N.S., Owusu Obeng, A., Sanderson, S.C., Gottesman, O. and Scott, S.A., 2014. Implementation and utilization of genetic testing in personalized medicine. Pharmacogenomics and personalized medicine, pp.227-240.
Jithesh, P.V. and Scaria, V., 2017. From genomes to genomic medicine: enabling personalized and precision medicine in the Middle East. Personalized Medicine, 14(5), pp.377-382.
Simmons, L.A., Dinan, M.A., Robinson, T.J. and Snyderman, R., 2012. Personalized medicine is more than genomic medicine: confusion over terminology impedes progress towards personalized healthcare. Personalized medicine, 9(1), pp.85-91.
Matchett, K.B., Lynam-Lennon, N., Watson, R.W. and Brown, J.A., 2017. Advances in precision medicine: tailoring individualized therapies.
McGowan, M.L., Settersten Jr, R.A., Juengst, E.T. and Fishman, J.R., 2014, February. Integrating genomics into clinical oncology: ethical and social challenges from proponents of personalized medicine. In Urologic Oncology: Seminars and Original Investigations (Vol. 32, No. 2, pp. 187-192). Elsevier.
Ahmed, M.N., Toor, A.S., O'Neil, K. and Friedland, D., 2017. Cognitive computing and the future of health care cognitive computing and the future of healthcare: the cognitive power of IBM Watson has the potential to transform global personalized medicine. IEEE pulse, 8(3), pp.4-9.
Juengst, E.T., Settersten, R.A., Fishman, J.R. and McGowan, M.L., 2012. After the Revolution? Ethical and Social Challenges in ‘Personalized Genomic Medicine ‘. Personalized medicine, 9(4), pp.429-439.
Cesuroglu, T., Van Ommen, B., Malats, N., Sudbrak, R., Lehrach, H. and Brand, A., 2012. Public health perspective: from personalized medicine to personal health. Personalized Medicine, 9(2), pp.115-119.
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