Evidence-based clinical engineering: Health information technology adverse events identification and classification with natural language processing

Alessio Luschi; Paolo Nesi; Ernesto Iadanza

doi:10.1016/j.heliyon.2023.e21723

Evidence-based clinical engineering: Health information technology adverse events identification and classification with natural language processing

Heliyon. 2023 Oct 31;9(11):e21723. doi: 10.1016/j.heliyon.2023.e21723. eCollection 2023 Nov.

Authors

Alessio Luschi¹, Paolo Nesi^{1

2}, Ernesto Iadanza³

Affiliations

¹ Department of Information Engineering, University of Florence, Via di S. Marta, 3, 50139, Firenze (FI), Italy.
² DISIT Lab, University of Florence, Via di S. Marta, 3, 50139, Firenze (FI), Italy.
³ Department of Medical Biotechnologies, University of Siena, Via A. Moro, 2, 53100, Siena (SI), Italy.

Abstract

The primary goal of this project is to create a framework to extract Real-World Evidence to support Health Technology Assessment, Health Technology Management, Evidence-Based Maintenance, and Post Market Surveillance (as outlined in the EU Medical Device Regulation 2017/745) of medical devices using Natural Language Processing (NLP) and Artificial Intelligence. An initial literature review on Spontaneous Reporting System databases, Health Information Technologies (HIT) fault classification, and Natural Language Processing has been conducted, from which it clearly emerges that adverse events related to HIT are increasing over time. The proposed framework uses NLP techniques and Explainable Artificial Intelligence models to automatically identify HIT-related adverse event reports. The designed model employs a pre-trained version of ClinicalBERT that has been fine-tuned and tested on 3,075 adverse event reports extracted from the FDA MAUDE database and manually labelled by experts.

Keywords: Clinical engineering; Health information technologies; Medical devices; Natural language processing; Real-world evidence.