AI-driven synthetic biology for non-small cell lung cancer drug effectiveness-cost analysis in intelligent assisted medical systems

Chang, Liu, Wu, Jia, Moustafa, Nour, Bashir, Ali Kashif ORCID: https://orcid.org/0000-0001-7595-2522 and Yu, Keping (2022) AI-driven synthetic biology for non-small cell lung cancer drug effectiveness-cost analysis in intelligent assisted medical systems. IEEE journal of biomedical and health informatics, 26 (10). pp. 5055-5066. ISSN 2168-2194

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Official URL: https://doi.org/10.1109/JBHI.2021.3133455

Abstract

According to statistics, in the 185 countries' 36 types of cancer, the morbidity and mortality of lung cancer take the first place, and non-small cell lung cancer (NSCLC) accounts for 85% of lung cancer (International Agency for Research on Cancer, 2018), (Bray et al., 2018). Significantly in many developing countries, limited medical resources and excess population seriously affect the diagnosis and treatment of alung cancer patients. The 21st century is an era of life medicine, big data, and information technology. Synthetic biology is known as the driving force of natural product innovation and research in this era. Based on the research of NSCLC targeted drugs, through the cross-fusion of synthetic biology and artificial intelligence, using the idea of bioengineering, we construct an artificial intelligence assisted medical system and propose a drug selection framework for the personalized selection of NSCLC patients. Under the premise of ensuring the efficacy, considering the economic cost of targeted drugs as an auxiliary decision-making factor, the system predicts the drug effectiveness-cost then. The experiment shows that our method can rely on the provided clinical data to screen drug treatment programs suitable for the patient's conditions and assist doctors in making an efficient diagnosis.

Item Type:	Article
Peer-reviewed:	Yes
Date Deposited:	03 Jan 2023 12:35
Publisher:	IEEE
Additional Information:	© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Divisions:	Faculties > Science and Engineering Research Centres > Centre for Advanced Computational Science
Subject terms:	Science & Technology, Technology, Life Sciences & Biomedicine, Computer Science, Information Systems, Computer Science, Interdisciplinary Applications, Mathematical & Computational Biology, Medical Informatics, Computer Science, Drugs, Medical diagnostic imaging, Costs, Bioinformatics, Lung cancer, Tumors, Synthetic biology, Non-small cell lung cancer (NSCLC), targeted drug therapy, synthetic biology, artificial intelligence medical system, drug effectiveness-cost analysis, MODEL, ALGORITHMS, DIAGNOSIS, PROGNOSIS, DISEASE, Humans, Carcinoma, Non-Small-Cell Lung, Lung Neoplasms, Biological Products, Artificial Intelligence, Costs and Cost Analysis, Synthetic Biology, Artificial Intelligence, Biological Products, Carcinoma, Non-Small-Cell Lung, Costs and Cost Analysis, Humans, Lung Neoplasms, Synthetic Biology
URI:	https://mmu-uat.leaf.cosector.com/id/eprint/631071
DOI:	https://doi.org/10.1109/JBHI.2021.3133455
ISSN	2168-2194
e-ISSN	2168-2208

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