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.

中文翻译：

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患者隐私保护：基于联邦学习和CBR生成可用的医疗计划

尽管与其他医院共享电子病历（EMR）对于提高临床决策效率的积极影响已被广泛认可，但由于患者隐私保护，电子病历共享的实际实施受到一定限制。本研究提出了一个三阶段框架，基于联邦学习和基于案例的推理（CBR）从多家医院检索医疗计划。我们证明，所提出的框架弥补了 CBR 的隐私保护弱点，并解决了医院之间的数据孤岛问题。