Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review.
Purpose. Artificial intelligence (AI) techniques are used in precision medicine to explore novel genotypes and phenotypes data. The main aims of precision medicine include early diagnosis, screening, and personalized treatment regime for a patient based on genetic-oriented features and characteristi...
| Publicado en: | BioMed Research International pp. 1 - 35 |
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| Autores principales: | , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
4/7/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=156201700&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156201700 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 4/7/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 156201700 156201700 156201700 10.1155/2022/7842566 156201700 ppf: 1 ppct: 34 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review. aug: au: Rezayi, Sorayya R Niakan Kalhori, Sharareh Saeedi, Soheila affil: Ph.D. Candidate in Medical Informatics, Health Information Management and Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran sug: subj: Artificial Intelligence Individualized Medicine Neoplasms Diagnosis Neoplasms Prevention and Control Neoplasms Therapy Early Detection of Cancer Methods Cancer Screening Methods Human Systematic Review PubMed Medline Embase Cochrane Library Sensitivity and Specificity Random Forest Decision Trees Deep Learning Data Analysis Software Breast Neoplasms Lung Neoplasms Genomics Gene Expression Mutation Phenotype Proteomics ab: Purpose. Artificial intelligence (AI) techniques are used in precision medicine to explore novel genotypes and phenotypes data. The main aims of precision medicine include early diagnosis, screening, and personalized treatment regime for a patient based on genetic-oriented features and characteristics. The main objective of this study was to review AI techniques and their effectiveness in neoplasm precision medicine. Materials and Methods. A comprehensive search was performed in Medline (through PubMed), Scopus, ISI Web of Science, IEEE Xplore, Embase, and Cochrane databases from inception to December 29, 2021, in order to identify the studies that used AI methods for cancer precision medicine and evaluate outcomes of the models. Results. Sixty-three studies were included in this systematic review. The main AI approaches in 17 papers (26.9%) were linear and nonlinear categories (random forest or decision trees), and in 21 citations, rule-based systems and deep learning models were used. Notably, 62% of the articles were done in the United States and China. R package was the most frequent software, and breast and lung cancer were the most selected neoplasms in the papers. Out of 63 papers, in 34 articles, genomic data like gene expression, somatic mutation data, phenotype data, and proteomics with drug-response which is functional data was used as input in AI methods; in 16 papers' (25.3%) drug response, functional data was utilized in personalization of treatment. The maximum values of the assessment indicators such as accuracy, sensitivity, specificity, precision, recall, and area under the curve (AUC) in included studies were 0.99, 1.00, 0.96, 0.98, 0.99, and 0.9929, respectively. Conclusion. The findings showed that in many cases, the use of artificial intelligence methods had effective application in personalized medicine. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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