Large Language Models Struggle in Token-Level Clinical Named Entity Recognition

• URL: http://arxiv.org/abs/2407.00731v2
• Date: Sat, 17 Aug 2024 00:59:55 GMT
• Title: Large Language Models Struggle in Token-Level Clinical Named Entity Recognition
• Authors: Qiuhao Lu, Rui Li, Andrew Wen, Jinlian Wang, Liwei Wang, Hongfang Liu,
• Abstract summary: Large Language Models (LLMs) have revolutionized various sectors, including healthcare where they are employed in diverse applications. Named Entity Recognition (NER) stands out as an essential task and it plays a crucial role in extracting relevant information from clinical texts. This study aims to bridge this gap by investigating the effectiveness of both proprietary and local LLMs in token-level clinical NER.
• Score: 12.972058180227194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have revolutionized various sectors, including healthcare where they are employed in diverse applications. Their utility is particularly significant in the context of rare diseases, where data scarcity, complexity, and specificity pose considerable challenges. In the clinical domain, Named Entity Recognition (NER) stands out as an essential task and it plays a crucial role in extracting relevant information from clinical texts. Despite the promise of LLMs, current research mostly concentrates on document-level NER, identifying entities in a more general context across entire documents, without extracting their precise location. Additionally, efforts have been directed towards adapting ChatGPT for token-level NER. However, there is a significant research gap when it comes to employing token-level NER for clinical texts, especially with the use of local open-source LLMs. This study aims to bridge this gap by investigating the effectiveness of both proprietary and local LLMs in token-level clinical NER. Essentially, we delve into the capabilities of these models through a series of experiments involving zero-shot prompting, few-shot prompting, retrieval-augmented generation (RAG), and instruction-fine-tuning. Our exploration reveals the inherent challenges LLMs face in token-level NER, particularly in the context of rare diseases, and suggests possible improvements for their application in healthcare. This research contributes to narrowing a significant gap in healthcare informatics and offers insights that could lead to a more refined application of LLMs in the healthcare sector.

Related papers

• Diagnostic Reasoning in Natural Language: Computational Model and Application [68.47402386668846]
  We investigate diagnostic abductive reasoning (DAR) in the context of language-grounded tasks (NL-DAR) We propose a novel modeling framework for NL-DAR based on Pearl's structural causal models. We use the resulting dataset to investigate the human decision-making process in NL-DAR.
  arXiv  Detail & Related papers  (2024-09-09T06:55:37Z)
• LLMs are not Zero-Shot Reasoners for Biomedical Information Extraction [13.965777046473885]
  Large Language Models (LLMs) are increasingly adopted for applications in healthcare. It is unclear how well LLMs perform on tasks that are traditionally pursued in the biomedical domain.
  arXiv  Detail & Related papers  (2024-08-22T09:37:40Z)
• 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)
• Large Language Models in the Clinic: A Comprehensive Benchmark [63.21278434331952]
  We build a benchmark ClinicBench to better understand large language models (LLMs) in the clinic. We first collect eleven existing datasets covering diverse clinical language generation, understanding, and reasoning tasks. We then construct six novel datasets and clinical tasks that are complex but common in real-world practice. We conduct an extensive evaluation of twenty-two LLMs under both zero-shot and few-shot settings.
  arXiv  Detail & Related papers  (2024-04-25T15:51:06Z)
• 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 in healthcare and medical domain: A review [4.456243157307507]
  Large language models (LLMs) provide proficient responses to free-text queries. This review explores the potential of LLMs to amplify the efficiency and effectiveness of diverse healthcare applications.
  arXiv  Detail & Related papers  (2023-12-12T20:54:51Z)
• Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models [48.07083163501746]
  Clinical natural language processing requires methods that can address domain-specific challenges. We propose an innovative, resource-efficient approach, ClinGen, which infuses knowledge into the process. Our empirical study across 7 clinical NLP tasks and 16 datasets reveals that ClinGen consistently enhances performance across various tasks.
  arXiv  Detail & Related papers  (2023-11-01T04:37:28Z)
• An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing [4.758617742396169]
  We present a comprehensive and systematic experimental study on prompt engineering for five clinical NLP tasks. We assessed the prompts proposed in recent literature, including simple prefix, simple cloze, chain of thought, and anticipatory prompts. We provide novel insights and guidelines for prompt engineering for LLMs in clinical NLP.
  arXiv  Detail & Related papers  (2023-09-14T19:35:00Z)
• 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)
• Are Large Language Models Ready for Healthcare? A Comparative Study on Clinical Language Understanding [12.128991867050487]
  Large language models (LLMs) have made significant progress in various domains, including healthcare. In this study, we evaluate state-of-the-art LLMs within the realm of clinical language understanding tasks.
  arXiv  Detail & Related papers  (2023-04-09T16:31:47Z)

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.