Annual Review of Biomedical Data Science最新文献

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Defining Phenotypes from Clinical Data to Drive Genomic Research. 从临床数据中定义表型以驱动基因组研究。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2018-07-01 Epub Date: 2018-04-25 DOI: 10.1146/annurev-biodatasci-080917-013335

Jamie R Robinson, Wei-Qi Wei, Dan M Roden, Joshua C Denny

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引用次数: 26

IF 6

Annual Review of Biomedical Data Science Pub Date : 2018-07-01 DOI: 10.1146/annurev-biodatasci-080917-013416

April Moreno Arellano, Wenrui Dai, Shuang Wang, Xiaoqian Jiang, Lucila Ohno-Machado

{"title":"Privacy Policy and Technology in Biomedical Data Science.","authors":"April Moreno Arellano, Wenrui Dai, Shuang Wang, Xiaoqian Jiang, Lucila Ohno-Machado","doi":"10.1146/annurev-biodatasci-080917-013416","DOIUrl":"10.1146/annurev-biodatasci-080917-013416","url":null,"abstract":"Privacyis an important consideration when sharing clinical data, which often contain sensitive information. Adequate protection to safeguard patient privacy and to increase public trust in biomedical research is paramount. This review covers topics in policy and technology in the context of clinical data sharing. We review policy articles related to (a) the Common Rule, HIPAA privacy and security rules, and governance; (b) patients' viewpoints and consent practices; and (c) research ethics. We identify key features of the revised Common Rule and the most notable changes since its previous version. We address data governance for research in addition to the increasing emphasis on ethical and social implications. Research ethics topics include data sharing best practices, use of data from populations of low socioeconomic status (SES), recent updates to institutional review board (IRB) processes to protect human subjects' data, and important concerns about the limitations of current policies to address data deidentification. In terms of technology, we focus on articles that have applicability in real world health care applications: deidentification methods that comply with HIPAA, data anonymization approaches to satisfy well-acknowledged issues in deidentified data, encryption methods to safeguard data analyses, and privacy-preserving predictive modeling. The first two technology topics are mostly relevant to methodologies that attempt to sanitize structured or unstructured data. The third topic includes analysis on encrypted data. The last topic includes various mechanisms to build statistical models without sharing raw data.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":"1 ","pages":"115-129"},"PeriodicalIF":6.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6497413/pdf/nihms-1021989.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37216509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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