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

Alessio Luschi, Paolo Nesi, Ernesto Iadanza

Abstract read

In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

A Multi-Criteria Decision Support Framework for Prioritizing the Repair of Failed Medical Devices: A Hospital Case Study.Healthcare (Basel, Switzerland) · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Alessio LuschiDepartment of Information Engineering, University of Florence, Via di S. Marta, 3, 50139, Firenze (FI), Italy.

Paolo NesiDepartment of Information Engineering, University of Florence, Via di S. Marta, 3, 50139, Firenze (FI), Italy.

Ernesto IadanzaDepartment of Medical Biotechnologies, University of Siena, Via A. Moro, 2, 53100, Siena (SI), Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Clinical engineeringHealth information technologiesMedical devicesNatural language processingReal-world evidence

Identifiers

PMID37954315

DOI10.1016/j.heliyon.2023.e21723

PMCPMC10638042

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.