We investigate different natural language processing (NLP) approaches based on contextualised word representations for the problem of early prediction of lung cancer using free-text patient medical notes of Dutch primary care physicians. Because lung cancer has a low prevalence in primary care, we also address the problem of classification under highly imbalanced classes. Specifically, we use large Transformer-based pretrained language models (PLMs) and investigate: 1) how \textit{soft prompt-tuning} -- an NLP technique used to adapt PLMs using small amounts of training data -- compares to standard model fine-tuning; 2) whether simpler static word embedding models (WEMs) can be more robust compared to PLMs in highly imbalanced settings; and 3) how models fare when trained on notes from a small number of patients. We find that 1) soft-prompt tuning is an efficient alternative to standard model fine-tuning; 2) PLMs show better discrimination but worse calibration compared to simpler static word embedding models as the classification problem becomes more imbalanced; and 3) results when training models on small number of patients are mixed and show no clear differences between PLMs and WEMs. All our code is available open source in \url{https://bitbucket.org/aumc-kik/prompt_tuning_cancer_prediction/}.

使用基于上下文的词表示法的自然语言处理方法，研究利用荷兰初级保健医生的患者医疗笔记进行早期肺癌预测的问题。使用大型Transformer预训练语言模型，调查软提示调整与传统模型微调，WEMs与PLMs的比较，以及在少数患者的笔记上训练模型的效果，发现软提示调整是标准模型微调的有效替代品，PLMs比WEMs表现更好，但当分类问题变得更加不平衡时更容易过拟合，而在小数量的患者笔记上训练模型的结果表现良好。

使用初级保健免费文本荷兰医疗笔记进行软提示调整以预测肺癌