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. 2023 Jan 1:46:108870.
doi: 10.1016/j.dib.2022.108870. eCollection 2023 Feb.

An annotated dataset for event-based surveillance of antimicrobial resistance

Nejat Arınık  1   2 , Wim Van Bortel  3 , Bahdja Boudoua  1   2 , Luca Busani  4 , Rémy Decoupes  1   2 , Roberto Interdonato  5   2 , Rodrique Kafando  1   2 , Esther van Kleef  6 , Mathieu Roche  5   2 , Mehtab Alam Syed  5   2 , Maguelonne Teisseire  1   2
Affiliations

An annotated dataset for event-based surveillance of antimicrobial resistance

Nejat Arınık et al. Data Brief. .

Abstract

This paper presents an annotated dataset used in the MOOD Antimicrobial Resistance (AMR) hackathon, hosted in Montpellier, June 2022. The collected data concerns unstructured data from news items, scientific publications and national or international reports, collected from four event-based surveillance (EBS) Systems, i.e. ProMED, PADI-web, HealthMap and MedISys. Data was annotated by relevance for epidemic intelligence (EI) purposes with the help of AMR experts and an annotation guideline. Extracted data were intended to include relevant events on the emergence and spread of AMR such as reports on AMR trends, discovery of new drug-bug resistances, or new AMR genes in human, animal or environmental reservoirs. This dataset can be used to train or evaluate classification approaches to automatically identify written text on AMR events across the different reservoirs and sectors of One Health (i.e. human, animal, food, environmental sources, such as soil and waste water) in unstructured data (e.g. news, tweets) and classify these events by relevance for EI purposes.

Keywords: Annotation; Antimicrobial resistance (AMR); Epidemiology; Text mining.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

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