Fig. 1: Comparison of distinct fine-tuning strategies.

An illustration comparing architectures utilizing distinct fine-tuning strategies experimented with in our study. a Compares the use of pretrained models alone versus fine-tuning the models using their self-supervised objectives. Self-supervised fine-tuning involves refining the pretrained model’s weights through its objective loss function(s) using the provided clinical notes. b Illustrates the differences between semi-supervised fine-tuning and foundation fine-tuning. Semi-supervised fine-tuning focuses on optimizing the model for a specific outcome of interest, whereas foundation fine-tuning employs a multi-task learning (MTL) objective, incorporating all available postoperative labels in the dataset.