October 05 ~ 06, 2024, Virtual Conference
Oliver Simonoski1, Dijana Capeska Bogatinoska2, 1Faculty of Communication Networks and Security, University of Information Science and Technology, St. Paul the Apostle, Ohrid, 6000, Macedonia, 2Faculty of Applied IT, Machine Intelligence and Robotics, University of Information Science and Technology, St. Paul the Apostle, Ohrid, 6000, Macedonia
In an era driven by information exchange, transparency holds crucial importance, particularly within the healthcare industry, where data integrity and confidentiality are paramount. This paper delves into the landscape of blockchain technology, elucidating its potential applications and existing solutions. Specifically, the study focuses on its integration into digital healthcare services, with a primary emphasis on Electronic Health Records (EHR). Leveraging blockchain-based implementations, patients gain the ability to securely store their medical data, facilitated through smart contracts capable of executing key functions such as Registration, Data Append, and Data Retrieve. The research addresses challenges in implementing blockchain in healthcare, proposing a solution using digital signatures and Role-Based Access Control for enhanced security. Additionally, insights from a survey and the development of a blockchain-based application underscore the potential impact of blockchain on patient-centric and secure healthcare services, promising substantial contributions to the healthcare system. By deploying Ethereum-based blockchain implementations, patients gain the ability to securely manage their medical data through smart contracts, revolutionizing the way healthcare records are stored and accessed. This multi-layered approach ensures data integrity and controlled access. The findings of this research underscore the impact blockchain technology can have on healthcare solutions, indicating a new era of patient-centric and secure healthcare services.
blockchain technology, distributed framework, electronic health records, Ethereum, smart contracts, eHealth, health data, data sharing.
Pranav Gupta, Cornell University, Ithaca, New York, USA
We present OpenStaxQA, an evaluation bench- mark specific to college-level educational applications based on 43 open-source college text- books in English, Spanish, and Polish, avail- able under a permissive Creative Commons license. We finetune and evaluate large language models (LLMs) with approximately 7 billion parameters on this dataset using quantized low rank adapters (QLoRa). Additionally, we also perform a zero-shot evaluation on the AI2 reasoning challenge dev dataset in order to check if OpenStaxQA can lead to an improved performance on other tasks. We also discuss broader impacts relevant to datasets such as OpenStaxQA.
Large language models, OpenStax.