Title: AutoPK: Leveraging LLMs and a Hybrid Similarity Metric for Advanced Retrieval of Pharmacokinetic Data from Complex Tables and Documents Show Chinese title

Title: AutoPK：利用大语言模型和混合相似性度量从复杂表格和文档中高级检索药代动力学数据

Abstract: Pharmacokinetics (PK) plays a critical role in drug development and regulatory decision-making for human and veterinary medicine, directly affecting public health through drug safety and efficacy assessments. However, PK data are often embedded in complex, heterogeneous tables with variable structures and inconsistent terminologies, posing significant challenges for automated PK data retrieval and standardization. AutoPK, a novel two-stage framework for accurate and scalable extraction of PK data from complex scientific tables. In the first stage, AutoPK identifies and extracts PK parameter variants using large language models (LLMs), a hybrid similarity metric, and LLM-based validation. The second stage filters relevant rows, converts the table into a key-value text format, and uses an LLM to reconstruct a standardized table. Evaluated on a real-world dataset of 605 PK tables, including captions and footnotes, AutoPK shows significant improvements in precision and recall over direct LLM baselines. For instance, AutoPK with LLaMA 3.1-70B achieved an F1-score of 0.92 on half-life and 0.91 on clearance parameters, outperforming direct use of LLaMA 3.1-70B by margins of 0.10 and 0.21, respectively. Smaller models such as Gemma 3-27B and Phi 3-12B with AutoPK achieved 2-7 fold F1 gains over their direct use, with Gemma's hallucination rates reduced from 60-95% down to 8-14%. Notably, AutoPK enabled open-source models like Gemma 3-27B to outperform commercial systems such as GPT-4o Mini on several PK parameters. AutoPK enables scalable and high-confidence PK data extraction, making it well-suited for critical applications in veterinary pharmacology, drug safety monitoring, and public health decision-making, while addressing heterogeneous table structures and terminology and demonstrating generalizability across key PK parameters. Code and data: https://github.com/hosseinsholehrasa/AutoPK Show Chinese abstract

Abstract: 药代动力学（PK）在人类和兽医学的药物开发和监管决策中起着关键作用，通过药物安全性和有效性评估直接影响公共健康。 然而， PK数据通常嵌入在结构复杂、异构的表格中，术语不一致，这给自动PK数据检索和标准化带来了重大挑战。 AutoPK是一种新的两阶段框架，用于从复杂的科学表格中准确且可扩展地提取PK数据。 在第一阶段，AutoPK使用大语言模型（LLMs）、混合相似性度量和基于LLM的验证来识别和提取PK参数变体。 第二阶段过滤相关行，将表格转换为键值文本格式，并使用LLM重建标准化表格。 在包含标题和脚注的真实世界605个PK表格数据集上进行评估，AutoPK在精度和召回率方面相比直接使用LLM基线有显著提升。 例如，使用LLaMA 3.1-70B的AutoPK在半衰期上的F1分数达到0.92，在清除率参数上达到0.91，分别比直接使用LLaMA 3.1-70B高出0.10和0.21。 较小的模型如Gemma 3-27B和Phi 3-12B结合AutoPK相比直接使用其性能提升了2-7倍，Gemma的幻觉率从60-95%降至8-14%。 值得注意的是，AutoPK使开源模型如Gemma 3-27B在多个PK参数上超越了商业系统如GPT-4o Mini。 AutoPK实现了可扩展且高置信度的PK数据提取，使其非常适合兽医药理学、药物安全性监测和公共卫生决策中的关键应用，同时解决了异构表格结构和术语问题，并在关键PK参数上表现出泛化能力。 代码和数据：https://github.com/hosseinsholehrasa/AutoPK