• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Frontiers in Public Health >Revolution in Health Care: How Will Data Science Impact Doctora??Patient Relationships?
【24h】

Revolution in Health Care: How Will Data Science Impact Doctora??Patient Relationships?

机译：卫生保健革命：数据科学将如何影响Doctora ??患者关系？

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

摘要

Over the last decade, a technical revolution has taken place in several industrial sectors, starting with internet companies. The computerization and interconnection of a wide variety of services and devices has facilitated the collection and storage of data, which has since increased by several orders of magnitude. The exploitation of these data has completely reshaped some services, such as Internet advertising, which has become largely personalized, while bringing with it its fair share of privacy issues. As data management and analysis have become central to many businesses, computer scientists have been called upon to provide tools capable of extracting knowledge from ever-growing, structured and unstructured databases. In this context, a paradigm shift occurred in data analysis as more data became available; with deep learning, data-driven approaches are nowadays often surpassing domain-specific approaches (1). Indeed, in very diverse predictive tasks, such as machine translation (2), object recognition (3) or speech recognition (4), general purpose models such as artificial neural networks have outperformed advanced algorithms developed by experts with domain-specific knowledge. Additionally, these machine learning algorithms have often reached experts’ performance level at various tasks, including medical diagnosis (5–8). However, these great successes have often been achieved at great expense: the acquisition of a large amount of structured and unstructured data.
机译：在过去的十年中，从互联网公司开始，在几个工业领域发生了技术革命。各种各样的服务和设备的计算机化和互连促进了数据的收集和存储，此后的数据量增加了几个数量级。对这些数据的利用已经完全重塑了某些服务，例如互联网广告，这些服务在很大程度上已经实现了个性化，同时也带来了相当一部分的隐私问题。随着数据管理和分析已成为许多企业的中心，计算机科学家已被要求提供能够从不断增长的结构化和非结构化数据库中提取知识的工具。在这种情况下，随着更多数据的可用，数据分析发生了范式转变。借助深度学习，如今数据驱动的方法通常会超越特定领域的方法（1）。实际上，在非常多样化的预测任务中，例如机器翻译（2），对象识别（3）或语音识别（4），通用模型（例如人工神经网络）的性能优于具有特定领域知识的专家开发的高级算法。此外，这些机器学习算法通常在各种任务（包括医学诊断）上达到专家的性能水平（5-8）。但是，这些巨大的成功通常要付出巨大的代价：获取大量的结构化和非结构化数据。

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