Data Analytics Framework for Identifying Relevant Adverse Events in Medical Software

Md Uddin, Mouzhi Ge

2023

Abstract

The clinical evaluation process is an ongoing and iterative process. Through clinical evaluation, the clinical performance and effectiveness of the medical device will be monitored. While the clinical evaluation process requires clinical data, these relevant data may come from different sources. One of the recommended sources is “medical device adverse event report database”, which is mentioned in several guidance documents, since the adverse event reports are useful to identify hazards caused by substances or technologies used in medical devices. They also contain signals on the new or unknown risks associated with medical devices. As the use of medical devices is increasing, new adverse event reports are being updated in a daily manner, thus, the size of the adverse event database is also increasing. It is difficult and time-consuming to collect and process data from multiple adverse event data sources and feed the data into the clinical evaluation process needs special consideration. In this paper, the feasibility of adopting a data analytic ecosystem to deal with a large amount of adverse event data is studied. As an output, a data analytics framework is proposed to process and classify adverse event texts. The whole process will significantly facilitate the clinical evaluation process for medical image analysis software.

Paper Citation

in Harvard Style

Uddin M. and Ge M. (2023). Data Analytics Framework for Identifying Relevant Adverse Events in Medical Software. In Proceedings of the 9th International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE, ISBN 978-989-758-645-3, SciTePress, pages 81-90. DOI: 10.5220/0012038900003476

in Bibtex Style

@conference{ict4awe23,
author={Md Uddin and Mouzhi Ge},
title={Data Analytics Framework for Identifying Relevant Adverse Events in Medical Software},
booktitle={Proceedings of the 9th International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE,},
year={2023},
pages={81-90},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012038900003476},
isbn={978-989-758-645-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE,
TI - Data Analytics Framework for Identifying Relevant Adverse Events in Medical Software
SN - 978-989-758-645-3
AU - Uddin M.
AU - Ge M.
PY - 2023
SP - 81
EP - 90
DO - 10.5220/0012038900003476
PB - SciTePress