保护患者隐私:基于联合学习和 CBR 生成可用的医疗方案

IF 8.2 2区 管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
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引用次数: 0

摘要

尽管与其他医院共享电子病历(EMR)对提高临床决策效率的有利影响已得到广泛认可,但由于患者隐私保护的原因,EMR 共享的实际实施在一定程度上受到限制。本研究提出了一种基于联合学习和病例推理(CBR)的三阶段框架,用于检索多家医院的医疗计划。我们证明了所提出的框架弥补了 CBR 在隐私保护方面的不足,并解决了医院间数据孤岛的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Patient privacy protection: Generating available medical treatment plans based on federated learning and CBR

Although the favorable impact of sharing electronic medical records (EMRs) with other hospitals on improving clinical decision-making efficiency is widely acknowledged, the actual implementation of EMR sharing has been limited to some extent because of patient privacy protections. This study proposes a three-stage framework to retrieve medical treatment plans from multiple hospitals based on federated learning and case-based reasoning (CBR). We demonstrate that the proposed framework compensates for the privacy protection weaknesses of CBR and solves the problem of data islands among hospitals.

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来源期刊
Information & Management
Information & Management 工程技术-计算机:信息系统
CiteScore
17.90
自引率
6.10%
发文量
123
审稿时长
1 months
期刊介绍: Information & Management is a publication that caters to researchers in the field of information systems as well as managers, professionals, administrators, and senior executives involved in designing, implementing, and managing Information Systems Applications.
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