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• Evaluation of Local Open-Source Large Language Models for Clinical Data Extraction
  Ardavan Kafaei
  Published on: 12 August 2024

• Published on: 12 August 2024

  Evaluation of Local Open-Source Large Language Models for Clinical Data Extraction

  • Ardavan Kafaei, General practitioner Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran

  Dear Editor,
  I am writing to commend the authors for their insightful manuscript titled "Evaluating local open-source large language models for data extraction from unstructured reports on mechanical thrombectomy in patients with ischemic stroke." (1). This study provides a rigorous evaluation of local open-source large language models (LLMs) in extracting clinical data from procedural reports, an area of increasing relevance given the rise in AI applications in healthcare.
  The manuscript presents a well-structured methodology for assessing the performance of three LLMs—Mixtral, Qwen, and BioMistral—on data extraction tasks from thrombectomy reports. The choice to focus on local models, as opposed to commercial counterparts, is particularly noteworthy due to the enhanced data privacy and security benefits it entails.
  Strengths of the Study:
  1. Comprehensive Approach: The use of a robust human-in-the-loop (HITL) annotation strategy to establish ground truth is a commendable approach. By incorporating expert validation into the workflow, the authors not only enhance the reliability of their results but also address one of the key challenges in data extraction—ensuring the accuracy of the extracted data.
  2. Clear Evaluation Metrics: The manuscript's use of precision, recall, and F1 score as metrics for evaluating model performance is appropriate and provides a detailed picture of each model's efficacy. The precision metrics, p...

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  Dear Editor,
  I am writing to commend the authors for their insightful manuscript titled "Evaluating local open-source large language models for data extraction from unstructured reports on mechanical thrombectomy in patients with ischemic stroke." (1). This study provides a rigorous evaluation of local open-source large language models (LLMs) in extracting clinical data from procedural reports, an area of increasing relevance given the rise in AI applications in healthcare.
  The manuscript presents a well-structured methodology for assessing the performance of three LLMs—Mixtral, Qwen, and BioMistral—on data extraction tasks from thrombectomy reports. The choice to focus on local models, as opposed to commercial counterparts, is particularly noteworthy due to the enhanced data privacy and security benefits it entails.
  Strengths of the Study:
  1. Comprehensive Approach: The use of a robust human-in-the-loop (HITL) annotation strategy to establish ground truth is a commendable approach. By incorporating expert validation into the workflow, the authors not only enhance the reliability of their results but also address one of the key challenges in data extraction—ensuring the accuracy of the extracted data.
  2. Clear Evaluation Metrics: The manuscript's use of precision, recall, and F1 score as metrics for evaluating model performance is appropriate and provides a detailed picture of each model's efficacy. The precision metrics, particularly for critical data points such as NIHSS scores and first series times, underscore the potential of LLMs in streamlining clinical data extraction processes.
  3. Time Efficiency: The study's findings on time savings achieved through LLM assistance, with an average reduction of 65.6% in manual extraction time, are compelling. This quantifiable benefit illustrates the practical utility of LLMs in real-world clinical settings, potentially alleviating the burden on medical professionals and accelerating the documentation process.
  Areas for Further Consideration:
  1. Generalizability: While the study provides valuable insights into the performance of LLMs in a specific setting and language (German), the generalizability of these findings to other languages and healthcare environments remains a consideration. Future studies could explore the performance of these models in different linguistic contexts and diverse clinical settings to broaden the applicability of the findings.
  2. Model Comparison: Although the manuscript includes a comparison of model performance, additional insights into the reasons behind the variability in precision, particularly for more complex data points like medication details and occluded vessels, would be beneficial. A deeper analysis of the models' strengths and limitations in handling different types of data could guide further refinements and improvements.
  3. External Validation: The use of an external dataset to evaluate model performance is a strong aspect of the study. However, incorporating validation from multiple external institutions could provide a more comprehensive assessment of model robustness and generalizability across different clinical environments.
  Conclusion:
  The study makes a significant contribution to the field of AI-assisted data extraction in healthcare by demonstrating the feasibility and effectiveness of local open-source LLMs in a secure and privacy-preserving manner. The findings highlight the potential of these models to support clinical documentation and research while addressing key concerns related to data privacy.
  References
  1. Meddeb A, Ebert P, Bressem KK, et al. Evaluating local open-source large language models for data extraction from unstructured reports on mechanical thrombectomy in patients with ischemic stroke. Journal of NeuroInterventional Surgery. Published Online First: 02 August 2024. doi: 10.1136/jnis-2024-022078

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  Conflict of Interest:
  None declared.

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