• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>JMIR Medical Informatics >Characterizing Artificial Intelligence Applications in Cancer Research: A Latent Dirichlet Allocation Analysis
【24h】

Characterizing Artificial Intelligence Applications in Cancer Research: A Latent Dirichlet Allocation Analysis

机译：在癌症研究中表征人工智能应用：潜在的Dirichlet分配分析

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Background Artificial intelligence (AI)–based therapeutics, devices, and systems are vital innovations in cancer control; particularly, they allow for diagnosis, screening, precise estimation of survival, informing therapy selection, and scaling up treatment services in a timely manner. Objective The aim of this study was to analyze the global trends, patterns, and development of interdisciplinary landscapes in AI and cancer research. Methods An exploratory factor analysis was conducted to identify research domains emerging from abstract contents. The Jaccard similarity index was utilized to identify the most frequently co-occurring terms. Latent Dirichlet Allocation was used for classifying papers into corresponding topics. Results From 1991 to 2018, the number of studies examining the application of AI in cancer care has grown to 3555 papers covering therapeutics, capacities, and factors associated with outcomes. Topics with the highest volume of publications include (1) machine learning, (2) comparative effectiveness evaluation of AI-assisted medical therapies, and (3) AI-based prediction. Noticeably, this classification has revealed topics examining the incremental effectiveness of AI applications, the quality of life, and functioning of patients receiving these innovations. The growing research productivity and expansion of multidisciplinary approaches are largely driven by machine learning, artificial neural networks, and AI in various clinical practices. Conclusions The research landscapes show that the development of AI in cancer care is focused on not only improving prediction in cancer screening and AI-assisted therapeutics but also on improving other corresponding areas such as precision and personalized medicine and patient-reported outcomes.
机译：背景技术人工智能（AI）基础的治疗剂，装置和系统是癌症控制的重要创新;特别是，它们允许诊断，筛查，精确估计生存，信息，及时缩放治疗服务。目的本研究的目的是分析AI和癌症研究中跨学科景观的全球趋势，模式和发展。方法进行探索性因子分析，以识别从抽象内容中出现的研究域。 Jaccard相似性指数用于识别最常见的共同术语。潜在的Dirichlet分配用于将文件分类为相应的主题。结果来自1991年至2018年，研究AI在癌症护理中应用AI的研究数量已占3555篇论文，涵盖了与结果相关的治疗，能力和因素。具有最高出版物的主题包括（1）机器学习，（2）AI辅助医疗疗法的比较效果评估，以及（3）基于AI的预测。明显的是，该分类揭示了旨在获得AI应用，生活质量和接受这些创新患者的寿命的增量效率的主题。日益增长的研究生产力和多学科方法的扩张主要由机器学习，人工神经网络和各种临床实践中的AI驱动。结论研究景观表明，癌症护理的AI的发展不仅重点是改善癌症筛查和AI辅助治疗的预测，还集中于改善诸如精确和个性化药物和患者报告的其他相应区域等其他地区。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号