Nazir Anam, Cheeema Muhammad Nadeem, Wang Ze
Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine.
Meta Radiol. 2023 Nov;1(3). doi: 10.1016/j.metrad.2023.100034. Epub 2023 Nov 11.
Missing data are a common problem for large cohort or longitudinal research and have been handled through data imputation. Based on simplified models such as linear or nonlinear interpolations, current imputation methods may not be accurate for real-life data such as biological and behavioral data. The purpose of this work was to explore the capability of ChatGPT, a powerful Large Language Model (LLM) developed by OpenAI, for biological and psychological data imputation. We tested the feasibility using data from the Human Connectome Project. Performance was evaluated by comparing the imputed data against known ground truth (GT) and measured with metrics like Pearson correlation coefficient (r), relative accuracy (MP), and mean absolute error (MAE). Comparative analyses with traditional imputation techniques are also conducted to demonstrate the superior efficacy of the ChatGPT as a data imputer. In summary, through customized data-to-text prompting engineering, ChatGPT can successfully capture intricate patterns and dependencies within biological data, resulting in precise imputations. Fine-tuning ChatGPT with domain-specific biological vocabulary with human in-loop as an interpreter enhances the accuracy and relevance of the imputations.
缺失数据是大型队列研究或纵向研究中常见的问题，通常通过数据插补来处理。基于线性或非线性插值等简化模型，当前的插补方法对于生物和行为数据等现实生活数据可能并不准确。这项工作的目的是探索由OpenAI开发的强大的大语言模型ChatGPT对生物和心理数据进行插补的能力。我们使用人类连接组计划的数据测试了其可行性。通过将插补数据与已知的真实数据（GT）进行比较来评估性能，并用皮尔逊相关系数（r）、相对准确率（MP）和平均绝对误差（MAE）等指标进行衡量。还与传统插补技术进行了比较分析，以证明ChatGPT作为数据插补工具的卓越功效。总之，通过定制的数据到文本提示工程，ChatGPT能够成功捕捉生物数据中的复杂模式和依赖性，从而实现精确插补。通过以人工作为解释器，使用特定领域的生物词汇对ChatGPT进行微调，可以提高插补的准确性和相关性。
