Leveraging transformers and large language models with antimicrobial prescribing data to predict sources of infection for electronic health record studies

Background Electronic health records frequently contain extensive unstructured free-text data, but extracting information accurately from these data and at scale is challenging. Using free-text from antibiotic prescribing data as an example, we investigate the performance of modern natural language processing methods (NLP) and large language models (LLMs) as tools for extracting features from medical records. Methods We used 938,150 hospital antibiotic prescriptions from Oxfordshire, UK. The 4000 most frequently used free-text indications justifying antibiotic use were labelled by clinical researchers into 11 categories describing the infection source/clinical syndrome being treated and used for model training. Traditional classification methods, fuzzy regex matching and n-grams with XGBoost, were compared against modern transformer models: we fine-tuned generic and domain-specific BERT models, fine-tuned GPT3.5, and investigated few-shot learning with GPT4. Models were evaluated on internal and external test datasets (2000 prescriptions each). Infection sources determined from ICD10 codes were also used for comparisons. Results In internal and external test datasets, the fine-tuned domain-specific Bio+Clinical BERT model averaged an F1 score of 0.97 and 0.98 respectively across the classes and outperformed the traditional regex (F1=0.71 and 0.74) and n-grams/XGBoost (F1=0.86 and 0.84). OpenAI’s GPT4 model achieved F1 scores of 0.71 and 0.86 without using labelled training data and a fine-tuned GPT3.5 model F1 scores of 0.95 and 0.97. Comparing infection sources extracted from ICD10 codes to those parsed from free-text indications, free-text indications revealed 31% more specific infection sources. Conclusion Modern transformer-based models can efficiently and accurately categorise semi-structured free-text in medical records, such as prescription free-text. Finetuned local transformer models outperform LLMs currently for structured tasks. Few shot LLMs match the performance of traditional NLP without the need for labelling. Transformer-based models have the potential to be used widely throughout medicine to analyse medical records more accurately, facilitating beter research and patient care. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Healthcare Associated Infections and Antimicrobial Resistance at Oxford University in partnership with the UK Health Security Agency (NIHR200915), and the NIHR Biomedical Research Centre, Oxford. DWE is a Big Data Institute Robertson Fellow. ASW is an NIHR Senior Investigator. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health or the UK Health Security Agency. KY is supported by the EPSRC Centre for Doctoral Training in Health Data Science (EP/S02428X/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The National Research Ethics Service South Central Oxford C Research Ethics Committee (19/SC/0403) and the national Confidentiality Advisory Group (19/CAG/0144) gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data analysed are available from the Infections in Oxfordshire Research Database (), subject to an application and research proposal meeting on the ethical and governance requirements of the Database. Labelled training and test datasets and the pre-trained BERT model are also available via an application to the Database.