Use of a Large Language Model to Identify and Classify Injuries With Free-Text Emergency Department Data

Giulia Lorenzoni¹, Dario Gregori¹, Silvia Bressan², Honoria Ocagli¹, Danila Azzolina³, Liviana Da Dalt², Paola Berchialla⁴

Affiliations

¹ Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy.
² Division of Pediatric Emergency Medicine, Department of Women's and Children's Health, University of Padova, Padova, Italy.
³ Department of Environmental and Preventive Science, University of Ferrara, Ferrara, Italy.
⁴ Centre for Biostatistics, Epidemiology and Public Health, Department of Clinical and Biological Sciences, University of Turin, Turin, Italy.

PMID: 38805230
PMCID: PMC11134210
DOI: 10.1001/jamanetworkopen.2024.13208

Use of a Large Language Model to Identify and Classify Injuries With Free-Text Emergency Department Data

Giulia Lorenzoni et al. JAMA Netw Open. 2024.

. 2024 May 1;7(5):e2413208.

doi: 10.1001/jamanetworkopen.2024.13208.

Authors

Giulia Lorenzoni¹, Dario Gregori¹, Silvia Bressan², Honoria Ocagli¹, Danila Azzolina³, Liviana Da Dalt², Paola Berchialla⁴

Affiliations

¹ Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy.
² Division of Pediatric Emergency Medicine, Department of Women's and Children's Health, University of Padova, Padova, Italy.
³ Department of Environmental and Preventive Science, University of Ferrara, Ferrara, Italy.
⁴ Centre for Biostatistics, Epidemiology and Public Health, Department of Clinical and Biological Sciences, University of Turin, Turin, Italy.

PMID: 38805230
PMCID: PMC11134210
DOI: 10.1001/jamanetworkopen.2024.13208

No abstract available

Plain language summary

This cross-sectional study assesses the accuracy, sensitivity, and specificity of a large language model used to process unstructured, non-English emergency department (ED) data in medical records.

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Conflict of interest statement

Conflict of Interest Disclosures: None reported.

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References

1. Lorenzoni G, Bressan S, Lanera C, Azzolina D, Da Dalt L, Gregori D. Analysis of unstructured text-based data using machine learning techniques: the case of pediatric emergency department records in Nicaragua. Med Care Res Rev. 2021;78(2):138-145. doi:10.1177/1077558719844123 - DOI - PubMed
1. Gianfrancesco MA, Goldstein ND. A narrative review on the validity of electronic health record-based research in epidemiology. BMC Med Res Methodol. 2021;21(1):234. doi:10.1186/s12874-021-01416-5 - DOI - PMC - PubMed
1. Azzolina D, Bressan S, Lorenzoni G, et al. . Pediatric injury surveillance from uncoded emergency department admission records in Italy: machine learning-based text-mining approach. JMIR Public Health Surveill. 2023;9:e44467. doi:10.2196/44467 - DOI - PMC - PubMed
1. Peden M, Oyebite K, Ozanne-Smith J, et al. , eds. World Report on Child Injury Prevention. World Health Organization; 2008. - PubMed
1. Rudnytskyi I. Openai: R wrapper for OpenAI API. R package version 0.4.1. Accessed April 15, 2024. https://github.com/irudnyts/openai

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Use of a Large Language Model to Identify and Classify Injuries With Free-Text Emergency Department Data

Affiliations

Use of a Large Language Model to Identify and Classify Injuries With Free-Text Emergency Department Data

Authors

Affiliations

Plain language summary

Conflict of interest statement

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