Although supervised machine learning is popular for information extraction
from clinical notes, creating large annotated datasets requires extensive
domain expertise and is time-consuming. Meanwhile, large language models (LLMs)
have demonstrated promising transfer learning capability. In this study, we
explored whether recent LLMs can reduce the need for large-scale data
annotations. We curated a manually-labeled dataset of 769 breast cancer
pathology reports, labeled with 13 categories, to compare zero-shot
classification capability of the GPT-4 model and the GPT-3.5 model with
supervised classification performance of three model architectures: random
forests classifier, long short-term memory networks with attention (LSTM-Att),
and the UCSF-BERT model. Across all 13 tasks, the GPT-4 model performed either
significantly better than or as well as the best supervised model, the LSTM-Att
model (average macro F1 score of 0.83 vs. 0.75). On tasks with high imbalance
between labels, the differences were more prominent. Frequent sources of GPT-4
errors included inferences from multiple samples and complex task design. On
complex tasks where large annotated datasets cannot be easily collected, LLMs
can reduce the burden of large-scale data labeling. However, if the use of LLMs
is prohibitive, the use of simpler supervised models with large annotated
datasets can provide comparable results. LLMs demonstrated the potential to
speed up the execution of clinical NLP studies by reducing the need for
curating large annotated datasets. This may result in an increase in the
utilization of NLP-based variables and outcomes in observational clinical
studies.

通过对 769 份乳腺癌病理报告进行人工标注的数据集的研究，我们比较了 GPT-4 模型和 GPT-3.5 模型的零样本分类能力与三种模型架构的监督分类性能，发现 GPT-4 模型在所有 13 个任务中要么显著优于要么与最佳监督模型的 LSTM-Att 模型相当（平均宏 F1 得分为 0.83 vs. 0.75）。研究还表明，如果难以使用大规模标注数据集，LLMs 可以减轻数据标注的负担，但使用带有大规模标注数据集的简单监督模型也可以提供可比较的结果，LLMs 可以加快临床自然语言处理研究的执行速度，提高 NLP 变量和结果在临床观察研究中的利用率。