Forecasting cyber security threats landscape and associated technical trends in telehealth using Bidirectional Encoder Representations from Transformers (BERT)

Abstract

Patient enablement and digital patient records have become crucial for the new order in healthcare delivery, as patients start to take an increased hands-on approach in their healthcare curation. A broader understanding of the efficacy and security of medical data in specific configurations of clinical interventions, applications, technological structures, logistics, and contextual situations is an urgent need. This research, therefore, analyzes 12,582 global patent grants to visualize the technical trend across seven major security threats identified by Kim et al., in their research titled, “Risk management-based security evaluation model for telemedicine systems”. The paper further investigates the adoption framework focusing on the stakeholders. The methodology used presents a systematic visualization across a large corpus of global patent grants from over 40 key patent offices to better understand the cybersecurity technology landscape and emerging trends for Telehealth. This visualization is enabled by a framework for intelligent collaborative patent mining which uses Bidirectional Encoder Representations from Transformers (BERT) for topic generation embedding text data to high dimensional mathematical space for contextual information mining within the patent grants space. The study identifies and categorizes across the seven major threat groups a significant amount of technical building blocks focused on the network and devices, however, it is found that development accounting for the end user (patient and associates) aspects such as diversity, education, emotions, etc. are yet to received attention a consideration during technical component development. The summarized results with a focus on human aspects among others will serve as a guide for using telemedicine to triage patients to the appropriate level and source of care.

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