Abeysinghe Rashmie, Jowah Gorbachev, Cui Licong, Lhatoo Samden D, Zhang Guo-Qiang
Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX.
AMIA Jt Summits Transl Sci Proc. 2025 Jun 10;2025:32-41. eCollection 2025.
Sudden Unexpected Death in Epilepsy (SUDEP) is a major cause of death for epilepsy patients having uncontrolled seizures. Understanding the complex neural circuits within the central nervous system is crucial for understanding the mechanisms underlying cardiorespiratory regulation, particularly in the context of SUDEP. This study explores the potential of GPT-4o, a cutting-edge language model, to automate the extraction of neural projections from scientific literature. We developed prompts to extract neuroscientific structures, extract projections, and perform synonym harmonization. Applying the approach to four neuroscientific articles, the method extracted 205 projections. A random sample of 100 projections identified was handed over to a domain expert for review where 95 were found to be correct. Therefore, GPT-4o was determined to be accurate in parsing complex scientific texts in extracting neural projections. Future work will involve extracting additional entities like techniques and species information for the projections identified.
癫痫性猝死（SUDEP）是癫痫发作未得到控制的患者的主要死因。了解中枢神经系统内复杂的神经回路对于理解心肺调节的潜在机制至关重要，尤其是在SUDEP的背景下。本研究探讨了前沿语言模型GPT-4o从科学文献中自动提取神经投射的潜力。我们开发了用于提取神经科学结构、提取投射以及进行同义词协调的提示。将该方法应用于四篇神经科学文章，该方法提取了205个投射。从识别出的投射中随机抽取100个样本交给领域专家进行审查，发现其中95个是正确的。因此，GPT-4o在解析复杂科学文本以提取神经投射方面被确定为准确的。未来的工作将涉及为识别出的投射提取技术和物种信息等其他实体。
