Evaluation of General Large Language Models in Contextually Assessing Semantic Concepts Extracted from Adult Critical Care Electronic Health Record Notes

• URL: http://arxiv.org/abs/2401.13588v1
• Date: Wed, 24 Jan 2024 16:52:37 GMT
• Title: Evaluation of General Large Language Models in Contextually Assessing Semantic Concepts Extracted from Adult Critical Care Electronic Health Record Notes
• Authors: Darren Liu, Cheng Ding, Delgersuren Bold, Monique Bouvier, Jiaying Lu, Benjamin Shickel, Craig S. Jabaley, Wenhui Zhang, Soojin Park, Michael J. Young, Mark S. Wainwright, Gilles Clermont, Parisa Rashidi, Eric S. Rosenthal, Laurie Dimisko, Ran Xiao, Joo Heung Yoon, Carl Yang, Xiao Hu
• Abstract summary: The purpose of this study was to evaluate the performance of Large Language Models (LLMs) in understanding and processing real-world clinical notes. The GPT family models have demonstrated considerable efficiency, evidenced by their cost-effectiveness and time-saving capabilities.
• Score: 17.648021186810663
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The field of healthcare has increasingly turned its focus towards Large Language Models (LLMs) due to their remarkable performance. However, their performance in actual clinical applications has been underexplored. Traditional evaluations based on question-answering tasks don't fully capture the nuanced contexts. This gap highlights the need for more in-depth and practical assessments of LLMs in real-world healthcare settings. Objective: We sought to evaluate the performance of LLMs in the complex clinical context of adult critical care medicine using systematic and comprehensible analytic methods, including clinician annotation and adjudication. Methods: We investigated the performance of three general LLMs in understanding and processing real-world clinical notes. Concepts from 150 clinical notes were identified by MetaMap and then labeled by 9 clinicians. Each LLM's proficiency was evaluated by identifying the temporality and negation of these concepts using different prompts for an in-depth analysis. Results: GPT-4 showed overall superior performance compared to other LLMs. In contrast, both GPT-3.5 and text-davinci-003 exhibit enhanced performance when the appropriate prompting strategies are employed. The GPT family models have demonstrated considerable efficiency, evidenced by their cost-effectiveness and time-saving capabilities. Conclusion: A comprehensive qualitative performance evaluation framework for LLMs is developed and operationalized. This framework goes beyond singular performance aspects. With expert annotations, this methodology not only validates LLMs' capabilities in processing complex medical data but also establishes a benchmark for future LLM evaluations across specialized domains.

Related papers

• MEDIC: Towards a Comprehensive Framework for Evaluating LLMs in Clinical Applications [2.838746648891565]
  We introduce MEDIC, a framework assessing Large Language Models (LLMs) across five critical dimensions of clinical competence. We apply MEDIC to evaluate LLMs on medical question-answering, safety, summarization, note generation, and other tasks. Results show performance disparities across model sizes, baseline vs medically finetuned models, and have implications on model selection for applications requiring specific model strengths.
  arXiv  Detail & Related papers  (2024-09-11T14:44:51Z)
• GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI [67.09501109871351]
  Large Vision-Language Models (LVLMs) are capable of handling diverse data types such as imaging, text, and physiological signals. GMAI-MMBench is the most comprehensive general medical AI benchmark with well-categorized data structure and multi-perceptual granularity to date. It is constructed from 284 datasets across 38 medical image modalities, 18 clinical-related tasks, 18 departments, and 4 perceptual granularities in a Visual Question Answering (VQA) format.
  arXiv  Detail & Related papers  (2024-08-06T17:59:21Z)
• SemioLLM: Assessing Large Language Models for Semiological Analysis in Epilepsy Research [45.2233252981348]
  Large Language Models have shown promising results in their ability to encode general medical knowledge. We test the ability of state-of-the-art LLMs to leverage their internal knowledge and reasoning for epilepsy diagnosis.
  arXiv  Detail & Related papers  (2024-07-03T11:02:12Z)
• Towards Automatic Evaluation for LLMs' Clinical Capabilities: Metric, Data, and Algorithm [15.627870862369784]
  Large language models (LLMs) are gaining increasing interests to improve clinical efficiency for medical diagnosis. We propose an automatic evaluation paradigm tailored to assess the LLMs' capabilities in delivering clinical services.
  arXiv  Detail & Related papers  (2024-03-25T06:17:54Z)
• Attribute Structuring Improves LLM-Based Evaluation of Clinical Text Summaries [62.32403630651586]
  Large language models (LLMs) have shown the potential to generate accurate clinical text summaries, but still struggle with issues regarding grounding and evaluation. Here, we explore a general mitigation framework using Attribute Structuring (AS), which structures the summary evaluation process. AS consistently improves the correspondence between human annotations and automated metrics in clinical text summarization.
  arXiv  Detail & Related papers  (2024-03-01T21:59:03Z)
• Asclepius: A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models [59.60384461302662]
  We introduce Asclepius, a novel benchmark for evaluating Medical Multi-Modal Large Language Models (Med-MLLMs) Asclepius rigorously and comprehensively assesses model capability in terms of distinct medical specialties and different diagnostic capacities. We also provide an in-depth analysis of 6 Med-MLLMs and compare them with 5 human specialists.
  arXiv  Detail & Related papers  (2024-02-17T08:04:23Z)
• AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator [69.51568871044454]
  We introduce textbfAI Hospital, a framework simulating dynamic medical interactions between emphDoctor as player and NPCs. This setup allows for realistic assessments of LLMs in clinical scenarios. We develop the Multi-View Medical Evaluation benchmark, utilizing high-quality Chinese medical records and NPCs.
  arXiv  Detail & Related papers  (2024-02-15T06:46:48Z)
• Large Language Models Illuminate a Progressive Pathway to Artificial Healthcare Assistant: A Review [16.008511195589925]
  Large language models (LLMs) have shown promising capabilities in mimicking human-level language comprehension and reasoning. This paper provides a comprehensive review on the applications and implications of LLMs in medicine.
  arXiv  Detail & Related papers  (2023-11-03T13:51:36Z)
• Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization [8.456700096020601]
  Large language models (LLMs) have shown promise in natural language processing (NLP), but their effectiveness on a diverse range of clinical summarization tasks remains unproven. In this study, we apply adaptation methods to eight LLMs, spanning four distinct clinical summarization tasks. A clinical reader study with ten physicians evaluates summary, completeness, correctness, and conciseness; in a majority of cases, summaries from our best adapted LLMs are either equivalent (45%) or superior (36%) compared to summaries from medical experts.
  arXiv  Detail & Related papers  (2023-09-14T05:15:01Z)
• Self-Verification Improves Few-Shot Clinical Information Extraction [73.6905567014859]
  Large language models (LLMs) have shown the potential to accelerate clinical curation via few-shot in-context learning. They still struggle with issues regarding accuracy and interpretability, especially in mission-critical domains such as health. Here, we explore a general mitigation framework using self-verification, which leverages the LLM to provide provenance for its own extraction and check its own outputs.
  arXiv  Detail & Related papers  (2023-05-30T22:05:11Z)
• Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation [48.87254340298189]
  We construct a new dataset named MedLane to support the development and evaluation of automated clinical language simplification approaches. We propose a new model called DECLARE that follows the human annotation procedure and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-12-04T06:09:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.