{"title":"Research on the application of data mining in the field of healthcare","authors":"Wanwan Ding, Juntao Fang","doi":"10.62051/4pdg6558","DOIUrl":null,"url":null,"abstract":"The healthcare big data industry is rapidly developing globally, and data mining and knowledge services in the healthcare field have become one of the core demands for its development. Data mining in healthcare is beneficial to improve the efficiency of diagnosis and treatment of patients, which is helpful to formulate more effective treatment plans and reduce medical costs. In this paper, we searched the core journals on China Knowledge Network and web of science by subject terms, and eliminated the irrelevant articles for literature counting. In this paper, the commonly used models and algorithms of data mining in healthcare are firstly elaborated; then the progress of the application of this technology in assisting medical tasks, optimizing resource allocation and improving health information services are respectively reviewed, summarizing the segmentation, classic algorithms and representative studies implied by each application. However, the application of data mining technology in healthcare also faces some problems, from data collection, to data cleaning, preprocessing, visualization, to the selection of algorithms and evaluation of results, each link is full of difficulties and challenges. Finally, this paper proposes future research directions such as diversifying data sources, strengthening security and privacy protection, developing visualization and analysis tools, accurately using big data to improve the service level of healthcare institutions, semanticizing electronic medical records mining, and improving cancer prevention. At the same time, data mining is deeply integrated with cloud computing, artificial intelligence and other fields to jointly promote scientific and technological progress in the field of health care.","PeriodicalId":515906,"journal":{"name":"Transactions on Economics, Business and Management Research","volume":"7 14","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions on Economics, Business and Management Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.62051/4pdg6558","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The healthcare big data industry is rapidly developing globally, and data mining and knowledge services in the healthcare field have become one of the core demands for its development. Data mining in healthcare is beneficial to improve the efficiency of diagnosis and treatment of patients, which is helpful to formulate more effective treatment plans and reduce medical costs. In this paper, we searched the core journals on China Knowledge Network and web of science by subject terms, and eliminated the irrelevant articles for literature counting. In this paper, the commonly used models and algorithms of data mining in healthcare are firstly elaborated; then the progress of the application of this technology in assisting medical tasks, optimizing resource allocation and improving health information services are respectively reviewed, summarizing the segmentation, classic algorithms and representative studies implied by each application. However, the application of data mining technology in healthcare also faces some problems, from data collection, to data cleaning, preprocessing, visualization, to the selection of algorithms and evaluation of results, each link is full of difficulties and challenges. Finally, this paper proposes future research directions such as diversifying data sources, strengthening security and privacy protection, developing visualization and analysis tools, accurately using big data to improve the service level of healthcare institutions, semanticizing electronic medical records mining, and improving cancer prevention. At the same time, data mining is deeply integrated with cloud computing, artificial intelligence and other fields to jointly promote scientific and technological progress in the field of health care.
医疗大数据产业正在全球范围内迅速发展,医疗领域的数据挖掘和知识服务已成为其发展的核心需求之一。医疗领域的数据挖掘有利于提高患者的诊断和治疗效率,有利于制定更有效的治疗方案,降低医疗成本。本文通过主题词检索中国知网和web of science上的核心期刊,剔除不相关的文章进行文献统计。本文首先阐述了数据挖掘在医疗卫生领域的常用模型和算法,然后分别回顾了该技术在辅助医疗任务、优化资源配置和改善医疗信息服务等方面的应用进展,总结了各项应用所蕴含的细分领域、经典算法和代表性研究。然而,数据挖掘技术在医疗卫生领域的应用也面临着一些问题,从数据采集,到数据清洗、预处理、可视化,再到算法选择和结果评估,每个环节都充满了困难和挑战。最后,本文提出了未来的研究方向,如丰富数据来源、加强安全和隐私保护、开发可视化分析工具、准确利用大数据提高医疗机构服务水平、电子病历挖掘语义化、提高癌症预防水平等。同时,将数据挖掘与云计算、人工智能等领域深度融合,共同推动医疗卫生领域的科技进步。