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We investigate different natural language processing (NLP) approaches basedon contextualised word representations for the problem of early prediction oflung cancer using free-text patient medical notes of Dutch primary carephysicians. Because lung cancer has a low prevalence in primary care, we alsoaddress 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 toadapt PLMs using small amounts of training data -- compares to standard modelfine-tuning; 2) whether simpler static word embedding models (WEMs) can be morerobust compared to PLMs in highly imbalanced settings; and 3) how models farewhen 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 simplerstatic word embedding models as the classification problem becomes moreimbalanced; and 3) results when training models on small number of patients aremixed and show no clear differences between PLMs and WEMs. All our code isavailable open source in\url{https://bitbucket.org/aumc-kik/prompt_tuning_cancer_prediction/}.

Soft-prompt tuning to predict lung cancer using primary care free-text Dutch medical notes