{"title":"保护患者隐私:基于联合学习和 CBR 生成可用的医疗方案","authors":"","doi":"10.1016/j.im.2023.103908","DOIUrl":null,"url":null,"abstract":"<div><p>Although the favorable impact of sharing electronic medical records<span> (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.</span></p></div>","PeriodicalId":56291,"journal":{"name":"Information & Management","volume":"61 7","pages":"Article 103908"},"PeriodicalIF":8.2000,"publicationDate":"2023-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Patient privacy protection: Generating available medical treatment plans based on federated learning and CBR\",\"authors\":\"\",\"doi\":\"10.1016/j.im.2023.103908\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Although the favorable impact of sharing electronic medical records<span> (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.</span></p></div>\",\"PeriodicalId\":56291,\"journal\":{\"name\":\"Information & Management\",\"volume\":\"61 7\",\"pages\":\"Article 103908\"},\"PeriodicalIF\":8.2000,\"publicationDate\":\"2023-12-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information & Management\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0378720623001568\",\"RegionNum\":2,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information & Management","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378720623001568","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
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.
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.