[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126666-en":3,"doc-seo-126666-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126666,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Towards a Universal Privacy Model for Electronic Health Record Systems - An Ontology and Machine Learning Approach","This paper proposes a universal privacy model for Electronic Health Record (EHR) systems, combining a conceptual privacy ontology with machine learning methods. It addresses core limitations in current EHR practices, including the difficulty of balancing privacy with accessibility, usability, and legal compliance. The approach targets efficient management and sharing of patients’ personal and sensitive data across different platform contexts. It further evaluates multiple BERT-based techniques to distinguish legitimate from illegitimate privacy policies, with DistilBERT achieving the highest accuracy for detecting inadequate policies.","Towards a Universal Privacy Model for Electronic Health Record Systems: An Ontology and Machine Learning Approach  \nThis is the Published version of the following publication  \nNowrozy, Raza, Ahmed, Khandakar, Wang, Hua and Mcintosh, Timothy (2023) Towards a Universal Privacy Model for Electronic Health Record Systems: An Ontology and Machine Learning Approach. Informatics, 10 (3) . ISSN 2227- 9709  \nThe publisher’s official version can be found at [https://www.mdpi.com/2227-9709/10/3/60](https://www.mdpi.com/2227-9709/10/3/60)[ ](https://www.mdpi.com/2227-9709/10/3/60)Note that access to this version may require subscription.  \nDownloaded from VU Research Repository [https://vuir.vu.edu.au/47933/](https://vuir.vu.edu.au/47933/)  \n informatics   \nArticle  \nTowards a Universal Privacy Model for Electronic Health  \nRecord Systems: An Ontology and Machine Learning Approach  \nRaza Nowrozy 1, *, Khandakar Ahmed 1, Hua Wang 1 and Timothy Mcintosh 2  \n1 College of Engineering and Science, Victoria University, Melbourne 3000, Australia; [khandakar.ahmed@vu.edu.au](khandakar.ahmed@vu.edu.au) (K.A.); [hua.wang@vu.edu.au](hua.wang@vu.edu.au) (H.W.)  \n2 Department of Computer Science and Information Technology, La Trobe University, Bundoora 3086, Australia; [t.mcintosh@latrobe.edu.au](t.mcintosh@latrobe.edu.au)  \n* Correspondence: [raza.nowrozy@live.vu.edu.au](raza.nowrozy@live.vu.edu.au)  \nAbstract: This paper proposed a novel privacy model for Electronic Health Records (EHR) systems utilizing a conceptual privacy ontology and Machine Learning (ML) methodologies. It underscores the challenges currently faced by EHR systems such as balancing privacy and accessibility, userfriendliness, and legal compliance. To address these challenges, the study developed a universal privacy model designed to efficiently manage and share patients' personal and sensitive data across different platforms, such as MHR and NHS systems. The research employed various BERT techniques to differentiate between legitimate and illegitimate privacy policies. Among them, Distil BERT emerged as the most accurate, demonstrating the potential of our ML-based approach to effectively identify inadequate privacy policies. This paper outlines future research directions, emphasizing the need for comprehensive evaluations, testing in real-world case studies, the investigation of adaptive frameworks, ethical implications, and fostering stakeholder collaboration. This research offers a pioneering approach towards enhancing healthcare information privacy, providing an innovative foundation for future work in this field.  \nCitation: Nowrozy, R.; Ahmed, K.; Wang, H.; Mcintosh, T. Towards a Universal Privacy Model for Electronic Health Record Systems:  \nAn Ontology and Machine Learning Approach. Informatics 2023, 10, 60 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)informatics10030060  \nAcademic Editor: Jiang Bian  \nReceived: 28 May 2023  \nRevised: 27 June 2023  \nAccepted: 5 July 2023  \nPublished: 11 July 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nKeywords: privacy; privacy policy; ontology; health information privacy; machine learning; natural language processing  \n1. Introduction  \nThe growing adoption of Electronic Health Records (EHRs) has led to a significant increase in privacy and security concerns [1–5] . Despite the implementation of numerous privacy and security measures, patients' privacy continues to be compromised, often due to unreliable information-sharing methods and inadequate privacy policies [1,6–10] . Highprofile data breaches in systems such as Australia's My Health Record (MHR) and the UK's National Health Servi","cbCairx5ZZhTi1yA","https://ap.wps.com/l/cbCairx5ZZhTi1yA","pdf",1026726,1,29,"English","en",105,"# Introduction\n## Privacy and security challenges in EHR systems\n## Role of context-sensitive privacy policies\n## Need for a novel universal privacy model","[{\"question\":\"What is the paper’s main goal for Electronic Health Record (EHR) systems?\",\"answer\":\"To build a universal privacy model that manages and shares patients’ personal and sensitive data efficiently while improving privacy protection across different platform contexts.\"},{\"question\":\"How does the proposed approach distinguish legitimate from illegitimate privacy policies?\",\"answer\":\"It uses machine learning techniques based on BERT variants, evaluating which models best separate legitimate and inadequate privacy policies.\"},{\"question\":\"Why is context-sensitive privacy policy important in EHR privacy?\",\"answer\":\"Because privacy settings and access controls should be tailored to the specific circumstances and sensitivity of the health information, with stricter controls for sensitive data.\"}]","Towards a Universal Privacy Model for Electronic Health Record Systems - 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