The increasing availability of unstructured clinical narratives in electronic health records (EHRs) has created new opportunities for automated disease characterization, cohort identification, and clinical decision support. However, modeling long, domain-specific clinical text remains challenging due to limited labeled data, severe class imbalance, and the high computational cost of adapting large pretrained language models. This study presents a GPT-based architecture for clinical text classification that adapts a pretrained decoder-only Transformer using a selective fine-tuning strategy. Rather than updating all model parameters, the majority of the GPT-2 backbone is frozen, and training is restricted to the final Transformer block, the final layer normalization, and a lightweight classification head. This approach substantially reduces the number of trainable parameters while preserving the representational capacity required to model complex clinical language. The proposed method is evaluated on radiology reports from the MIMIC-IV-Note dataset using uncertainty-aware CheXpert-style labels derived directly from report text. Experiments cover multiple problem formulations, including multi-label classification of radiographic findings, binary per-label classification under different uncertainty assumptions, and aggregate disease outcome prediction. Across varying dataset sizes, the model exhibits stable convergence behavior and strong classification performance, particularly in settings dominated by non-mention and negated findings. Overall, the results indicate that selective fine-tuning of pretrained generative language models provides an efficient and effective pathway for clinical text classification, enabling scalable adaptation to real-world EHR data while significantly reducing computational complexity.

翻译：电子健康记录（EHR）中非结构化临床叙事数据的日益增多，为自动化疾病表征、队列识别和临床决策支持创造了新的机遇。然而，由于标注数据有限、类别严重不平衡以及适应大型预训练语言模型的高计算成本，对长篇幅、领域特定的临床文本进行建模仍然具有挑战性。本研究提出了一种基于GPT的临床文本分类架构，该架构采用选择性微调策略来适应预训练的仅解码器Transformer模型。该方法不更新所有模型参数，而是冻结GPT-2主干网络的大部分权重，并将训练限制在最后的Transformer块、最后的层归一化层以及一个轻量级分类头上。这种方法显著减少了可训练参数的数量，同时保留了建模复杂临床语言所需的表征能力。所提出的方法在MIMIC-IV-Note数据集的放射学报告上进行了评估，使用了直接从报告文本中提取的、具有不确定性感知的CheXpert风格标签。实验涵盖了多种问题设定，包括放射学发现的多标签分类、不同不确定性假设下的二元逐标签分类以及聚合疾病结果预测。在不同数据集规模下，该模型表现出稳定的收敛行为和强大的分类性能，尤其是在以未提及和否定发现为主的情境中。总体而言，结果表明，对预训练生成语言模型进行选择性微调为临床文本分类提供了一条高效且有效的途径，使其能够可扩展地适应现实世界的EHR数据，同时显著降低计算复杂度。