Abstract
Accurate representation of medical information is crucial for patient safety, yet artificial intelligence (AI) systems, such as Large Language Models (LLMs), encounter challenges in error-free clinical text interpretation. This paper presents a novel approach submitted to the MEDIQA-CORR 2024 shared task (Ben Abacha et al., 2024a), focusing on the automatic correction of single-word errors in clinical notes. Unlike LLMs that rely on extensive generic data, our method emphasizes extracting contextually relevant information from available clinical text data. Leveraging an ensemble of extractive and abstractive question-answering approaches, we construct a supervised learning framework with domain-specific feature engineering. Our methodology incorporates domain expertise to enhance error correction accuracy. By integrating domain expertise and prioritizing meaningful information extraction, our approach underscores the significance of a human-centric strategy in adapting AI for healthcare.
Abstract (translated)
准确地描述医学信息对患者安全至关重要，然而大型语言模型（LLMs）等人工智能系统在无错误地解释临床文本方面遇到了挑战。本文提交给MEDIQA-CORR 2024共享任务（Ben Abacha等人，2024a），重点关注自动纠正临床笔记中单个单词错误的创新方法。与依赖广泛通用数据的大语言模型不同，我们的方法强调从可用临床文本数据中提取相关信息。利用提取式和抽象式问题回答方法的集成，我们构建了一个领域特定特征工程的超集学习框架。我们的方法结合了领域专业知识以提高错误纠正准确性。通过将领域专业知识和强调有意义信息提取，我们的方法突出了在将AI适应 healthcare 时采用以人为中心的策略的重要性。
URL
https://arxiv.org/abs/2404.17999
