Deep Learning Transformer Models for Building a Comprehensive and Real-time Trauma Observatory: Development and Validation Study

Chenais, Gabrielle; Gil‐Jardine, Cédric; Touchais, Hélène; Fernandez, Marta Avalos; Contrand, Benjamin; Tellier, Edwige; Combes, X.; Bourdois, Loïck; Revel, Philippe; Lagarde, Emmanuel

doi:10.2196/40843

Cited by 4 publications

(1 citation statement)

References 29 publications

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“…Before of any data sharing researchers face the critical task of developing and integrating methods that mask sensitive data, guaranteeing protection against any unauthorized access [4]. Our team was recently faced with this challenge in a project aimed at classifying clinical notes from emergency services to extract the necessary information for the establishment of a trauma observatory [5].…”

Section: Introductionmentioning

confidence: 99%

Harnessing Moderate-Sized Language Models for Reliable Patient Data De-identification in Emergency Department Records: An Evaluation of Strategies and Performance (Preprint)

Dorémus,

Russon,

Contrand

et al. 2024

Preprint

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BACKGROUND The digitization of healthcare, facilitated by the adoption of electronic health record (EHR) systems, has revolutionized data-driven medical research and patient care. While this digital transformation offers substantial benefits in healthcare efficiency and accessibility, it concurrently raises significant concerns over privacy and data security. Initially, the journey towards protecting patient data de-identification saw the transition from rule-based systems to more mixed approaches including machine learning for de-identifying patient data. Subsequently, the emergence of Large Language Models (LLMs) has represented a further opportunity in this domain, offering unparalleled potential for enhancing the accuracy of context-sensitive de-identification. However, despite LLMs offering significant potential, the deployment of the most advanced models in hospital environments is frequently hindered by data security issues and the extensive hardware resources required. OBJECTIVE The objective of our study is to design, implement, and evaluate de-identification algorithms by employing fine-tuning of moderate-sized open-source language models, ensuring their suitability for production inference tasks on personal computers. METHODS We aimed at replacing personal identifying information (PII) with generic placeholders or labeling non-PII texts as 'ANONYMOUS', ensuring privacy while preserving textual integrity. Our dataset, derived from over 425,000 clinical notes from the adult emergency department of the Bordeaux University Hospital in France, underwent independent double annotation by two experts to create a reference for model validation with 3,000 clinical notes randomly selected. Three open-source language models of manageable size were selected for their feasibility in hospital settings: Llama 2 7B, Mistral 7B, and Mixtral 8x7B. Fine-tuning utilized the quantized Low-Rank Adaptation (qLoRA) technique. Evaluation focused on PII-level (Recall, Precision and F1-Score) and clinical note-level metrics (Recall and BLEU metric), assessing de-identification effectiveness and content preservation. RESULTS The generative model Mistral 7B demonstrated the highest performance with an overall F1-score of 0.9673 (vs. 0.8750 for Llama 2 and 0.8686 for Mistral 8x7B). At the clinical notes level, the same model achieved an overall recall of 0.9326 (vs. 0.6888 for Llama 2 and 0.6417 for Mistral 8x7B).This rate increased to 0.9915 for the anonymization of names with Mistral 7B. Four notes out of the total 3000 failed to be fully anonymized for names: in one case, the non-anonymized name belonged to a patient, while in the other cases, it belonged to medical staff. Beyond the fifth epoch, the BLEU score consistently exceeded 0.9864, indicating no significant text alteration due to the process. CONCLUSIONS Our research underscores the significant capabilities of generative NLP models, with Mistral 7B standing out for its superior ability to de-identify clinical texts efficiently. Achieving notable performance metrics, Mistral 7B operates effectively without requiring high-end computational resources. These methods pave the way for a broader availability of anonymized clinical texts, enabling their use for research purposes and the optimization of the healthcare system.

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Section: Introductionmentioning

confidence: 99%

Harnessing Moderate-Sized Language Models for Reliable Patient Data De-identification in Emergency Department Records: An Evaluation of Strategies and Performance (Preprint)

Dorémus,

Russon,

Contrand

et al. 2024

Preprint

Self Cite

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The research study on intelligent system for electronic health record that handles interpretation of child patient records

Gahane,

Verma

2024

AIP Conference Proceedings

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The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review

Preiksaitis,

Ashenburg,

Bunney

et al. 2024

JMIR Med Inform

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Background Artificial intelligence (AI), more specifically large language models (LLMs), holds significant potential in revolutionizing emergency care delivery by optimizing clinical workflows and enhancing the quality of decision-making. Although enthusiasm for integrating LLMs into emergency medicine (EM) is growing, the existing literature is characterized by a disparate collection of individual studies, conceptual analyses, and preliminary implementations. Given these complexities and gaps in understanding, a cohesive framework is needed to comprehend the existing body of knowledge on the application of LLMs in EM. Objective Given the absence of a comprehensive framework for exploring the roles of LLMs in EM, this scoping review aims to systematically map the existing literature on LLMs’ potential applications within EM and identify directions for future research. Addressing this gap will allow for informed advancements in the field. Methods Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria, we searched Ovid MEDLINE, Embase, Web of Science, and Google Scholar for papers published between January 2018 and August 2023 that discussed LLMs’ use in EM. We excluded other forms of AI. A total of 1994 unique titles and abstracts were screened, and each full-text paper was independently reviewed by 2 authors. Data were abstracted independently, and 5 authors performed a collaborative quantitative and qualitative synthesis of the data. Results A total of 43 papers were included. Studies were predominantly from 2022 to 2023 and conducted in the United States and China. We uncovered four major themes: (1) clinical decision-making and support was highlighted as a pivotal area, with LLMs playing a substantial role in enhancing patient care, notably through their application in real-time triage, allowing early recognition of patient urgency; (2) efficiency, workflow, and information management demonstrated the capacity of LLMs to significantly boost operational efficiency, particularly through the automation of patient record synthesis, which could reduce administrative burden and enhance patient-centric care; (3) risks, ethics, and transparency were identified as areas of concern, especially regarding the reliability of LLMs’ outputs, and specific studies highlighted the challenges of ensuring unbiased decision-making amidst potentially flawed training data sets, stressing the importance of thorough validation and ethical oversight; and (4) education and communication possibilities included LLMs’ capacity to enrich medical training, such as through using simulated patient interactions that enhance communication skills. Conclusions LLMs have the potential to fundamentally transform EM, enhancing clinical decision-making, optimizing workflows, and improving patient outcomes. This review sets the stage for future advancements by identifying key research areas: prospective validation of LLM applications, establishing standards for responsible use, understanding provider and patient perceptions, and improving physicians’ AI literacy. Effective integration of LLMs into EM will require collaborative efforts and thorough evaluation to ensure these technologies can be safely and effectively applied.

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Deep Learning Transformer Models for Building a Comprehensive and Real-time Trauma Observatory: Development and Validation Study

Cited by 4 publications

References 29 publications

Harnessing Moderate-Sized Language Models for Reliable Patient Data De-identification in Emergency Department Records: An Evaluation of Strategies and Performance (Preprint)

Harnessing Moderate-Sized Language Models for Reliable Patient Data De-identification in Emergency Department Records: An Evaluation of Strategies and Performance (Preprint)

The research study on intelligent system for electronic health record that handles interpretation of child patient records

The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review

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