神经网络技术在提取新医学知识和提高患者精确决策方面的能力

IF 1 Q4 PHARMACOLOGY & PHARMACY
L. Yasnitsky, A. Dumler, F. Cherepanov, V. L. Yasnitsky, Natalia A. Uteva
{"title":"神经网络技术在提取新医学知识和提高患者精确决策方面的能力","authors":"L. Yasnitsky, A. Dumler, F. Cherepanov, V. L. Yasnitsky, Natalia A. Uteva","doi":"10.1080/23808993.2021.1993595","DOIUrl":null,"url":null,"abstract":"ABSTRACT Objectives Currently, there is an active use of neural networks in various areas of human activity. Problems of recognition, diagnostics, optimization, forecasting, control, as well as obtaining new scientific knowledge are successfully solved. However, in medicine, due to the particular complexity of the human body, neural networks are mainly used only for solving the simplest problems of classification and diagnostics. The purpose of this article is to show that the possibilities of neural network modeling in medicine are much wider. Methods This goal is achieved by using special mathematical techniques developed by the authors that allow us to overcome these difficulties. Results The intellectual system created in this way made it possible to reveal a number of interesting scientific knowledge, which sometimes does not coincide with the generally accepted ideas of doctors. Virtual experiments conducted on computer models of patients using our intelligent system clearly demonstrate which lifestyle and medication variations can benefit a particular patient in the long term, and which can cause harm. Conclusion Our intellectual system allows us to identify new scientific knowledge, to carry out long-term forecasting of the development of the disease, to select the optimal courses of treatment and prevention of diseases.","PeriodicalId":12124,"journal":{"name":"Expert Review of Precision Medicine and Drug Development","volume":"27 1","pages":"70 - 78"},"PeriodicalIF":1.0000,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Capabilities of neural network technologies for extracting new medical knowledge and enhancing precise decision making for patients\",\"authors\":\"L. Yasnitsky, A. Dumler, F. Cherepanov, V. L. Yasnitsky, Natalia A. Uteva\",\"doi\":\"10.1080/23808993.2021.1993595\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT Objectives Currently, there is an active use of neural networks in various areas of human activity. Problems of recognition, diagnostics, optimization, forecasting, control, as well as obtaining new scientific knowledge are successfully solved. However, in medicine, due to the particular complexity of the human body, neural networks are mainly used only for solving the simplest problems of classification and diagnostics. The purpose of this article is to show that the possibilities of neural network modeling in medicine are much wider. Methods This goal is achieved by using special mathematical techniques developed by the authors that allow us to overcome these difficulties. Results The intellectual system created in this way made it possible to reveal a number of interesting scientific knowledge, which sometimes does not coincide with the generally accepted ideas of doctors. Virtual experiments conducted on computer models of patients using our intelligent system clearly demonstrate which lifestyle and medication variations can benefit a particular patient in the long term, and which can cause harm. Conclusion Our intellectual system allows us to identify new scientific knowledge, to carry out long-term forecasting of the development of the disease, to select the optimal courses of treatment and prevention of diseases.\",\"PeriodicalId\":12124,\"journal\":{\"name\":\"Expert Review of Precision Medicine and Drug Development\",\"volume\":\"27 1\",\"pages\":\"70 - 78\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2021-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Expert Review of Precision Medicine and Drug Development\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/23808993.2021.1993595\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"PHARMACOLOGY & PHARMACY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert Review of Precision Medicine and Drug Development","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/23808993.2021.1993595","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 2

摘要

摘要目的目前,神经网络在人类活动的各个领域都得到了积极的应用。成功地解决了识别、诊断、优化、预测、控制以及获得新的科学知识的问题。然而,在医学中,由于人体的特殊复杂性,神经网络主要只用于解决最简单的分类和诊断问题。本文的目的是表明神经网络建模在医学中的可能性要大得多。方法这一目标是通过使用作者开发的特殊数学技术来实现的,这些技术使我们能够克服这些困难。结果以这种方式创造的知识系统使揭示一些有趣的科学知识成为可能,这些知识有时与医生普遍接受的想法不一致。使用我们的智能系统在患者的计算机模型上进行的虚拟实验清楚地表明,哪些生活方式和药物变化可以使特定患者长期受益,哪些会造成伤害。结论我们的智能系统使我们能够识别新的科学知识,对疾病的发展进行长期预测,选择最佳的治疗和预防方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Capabilities of neural network technologies for extracting new medical knowledge and enhancing precise decision making for patients
ABSTRACT Objectives Currently, there is an active use of neural networks in various areas of human activity. Problems of recognition, diagnostics, optimization, forecasting, control, as well as obtaining new scientific knowledge are successfully solved. However, in medicine, due to the particular complexity of the human body, neural networks are mainly used only for solving the simplest problems of classification and diagnostics. The purpose of this article is to show that the possibilities of neural network modeling in medicine are much wider. Methods This goal is achieved by using special mathematical techniques developed by the authors that allow us to overcome these difficulties. Results The intellectual system created in this way made it possible to reveal a number of interesting scientific knowledge, which sometimes does not coincide with the generally accepted ideas of doctors. Virtual experiments conducted on computer models of patients using our intelligent system clearly demonstrate which lifestyle and medication variations can benefit a particular patient in the long term, and which can cause harm. Conclusion Our intellectual system allows us to identify new scientific knowledge, to carry out long-term forecasting of the development of the disease, to select the optimal courses of treatment and prevention of diseases.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
2.30
自引率
0.00%
发文量
9
期刊介绍: Expert Review of Precision Medicine and Drug Development publishes primarily review articles covering the development and clinical application of medicine to be used in a personalized therapy setting; in addition, the journal also publishes original research and commentary-style articles. In an era where medicine is recognizing that a one-size-fits-all approach is not always appropriate, it has become necessary to identify patients responsive to treatments and treat patient populations using a tailored approach. Areas covered include: Development and application of drugs targeted to specific genotypes and populations, as well as advanced diagnostic technologies and significant biomarkers that aid in this. Clinical trials and case studies within personalized therapy and drug development. Screening, prediction and prevention of disease, prediction of adverse events, treatment monitoring, effects of metabolomics and microbiomics on treatment. Secondary population research, genome-wide association studies, disease–gene association studies, personal genome technologies. Ethical and cost–benefit issues, the impact to healthcare and business infrastructure, and regulatory issues.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信