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Data breaches in healthcare: security mechanisms for attack mitigation
Lili Nemec Zlatolas, Tatjana Welzer Družovec, Lenka Lhotska, 2024, izvirni znanstveni članek

Opis: The digitalisation of healthcare has increased the risk of cyberattacks in this sector, targeting sensitive personal information. In this paper, we conduct a systematic review of existing solutions for data breach mitigation in healthcare, analysing 99 research papers. There is a growing trend in research emphasising the security of electronic health records, data storage, access control, and personal health records. The analysis identified the adoption of advanced technologies, including Blockchain and Artificial Intelligence, alongside encryption in developing resilient solutions. These technologies lay the foundations for addressing the prevailing cybersecurity threats, with a particular focus on hacking or malicious attacks, followed by unauthorised access. The research highlights the development of strategies to mitigate data breaches and stresses the importance of technological progress in strengthening data security. The paper outlines future directions, highlighting the need for continuous technological progress and identifying the gaps in the attack mitigations.
Ključne besede: data security, privacy, sensitive personal information, electronic health records, cybersecurity
Objavljeno v DKUM: 23.08.2024; Ogledov: 109; Prenosov: 6
Celotno besedilo (1,51 MB)

Machine learning helps physicians in diagnosing of mitral valve prolapse
Petra Povalej Bržan, Mitja Lenič, Milan Zorman, Peter Kokol, Lenka Lhotska, Rado Pišot, 2003, izvirni znanstveni članek

Opis: In this paper we present a multimethod approach for induction of a specific class of classifiers, which can assist physicians in medical diagnosing in the case of mitral valve prolapse. Mitral valve prolapse is one of the most controversial prevalent cardiac condition and may affect up to ten percent of the population and in the worst case results in sudden death. MultiVeDec is a general framework enabling researchers to generate various intelligent tools based on machine learning. In this paper we focused on various decision tree methods, which are capable of extracting knowledge in a form closer to human perception, a feature that is very important in medical field. The experiment included classifiers with various classical single method approaches, evolutionary approaches, hybrid approaches and also our newest multimethod approach. The main concern of the latest approach is to find a way to enable dynamic combination of methodologies to the somehow quasi unified knowledge representation. The proposed multimethod approach was capable to outperform all other tested approaches by producing classifier for diagnosing mitral valve prolapse with the highest overall and average class accuracy. More importantly, it was also capable to find some new knowledge important in diagnosing of mitral valve prolapse.
Objavljeno v DKUM: 01.06.2012; Ogledov: 1993; Prenosov: 52
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