Neural Language Model for Automated Classification of Electronic Medical Records at the Emergency Room. The Significant Benefit of Unsupervised Generative Pre-training

In order to build a national injury surveillance system based on emergency room (ER) visits we are developing a coding system to classify their causes from clinical notes content. Supervised learning techniques have shown good results in this area but require to manually build a large learning annotated dataset. New levels of performance have been recently achieved in neural language models (NLM) with the use of models based on the Transformer architecture with an unsupervised generative pre-training step. Our hypothesis is that methods involving a generative self-supervised pre-training step significantly reduce the number of annotated samples required for supervised fine-tuning. In this case study, we assessed whether we could predict from free text clinical notes whether a visit was the consequence of a traumatic or a non-traumatic event. We compared two strategies: Strategy A consisted in training the GPT-2 NLM on the full 161 930 samples dataset with all labels (trauma/non-trauma). In Strategy B, we split the training dataset in two parts, a large one of 151 930 samples without any label for the self-supervised pre-training phase and a smaller one (up to 10 000 samples) for the supervised fine-tuning with labels. While strategy A needed to process 40 000 samples to achieve good performance (AUC>0.95), strategy B needed only 500 samples, a gain of 80. Moreover, an AUC of 0.93 was measured with only 30 labeled samples processed 3 times (3 epochs). To conclude, it is possible to adapt a multi-purpose NLM model such as the GPT-2 to create a powerful tool for classification of free-text notes with the need of a very small number of labeled samples. Only two modalities (trauma/non-trauma) were predicted for this case study but the same method can be applied for multimodal classification tasks such as diagnosis/disease terminologies.