We present our work on building large scale sequence-to-sequence models for generating clinical note from patient-doctor conversation. This is formulated as an abstractive summarization task for which we use encoder-decoder transformer model with pointer-generator. We discuss various modeling enhancements to this baseline model which include using subword and multiword tokenization scheme, prefixing the targets with a chain-of-clinical-facts, and training with contrastive loss that is defined over various candidate summaries. We also use flash attention during training and query chunked attention during inference to be able to process long input and output sequences and to improve computational efficiency. Experiments are conducted on a dataset containing about 900K encounters from around 1800 healthcare providers covering 27 specialties. The results are broken down into primary care and non-primary care specialties. Consistent accuracy improvements are observed across both of these categories.

We introduce MedicalSum, a transformer-based sequence-to-sequence architecture for summarizing medical conversations by integrating medical domain knowledge from the Unified Medical Language System (UMLS). The novel knowledge augmentation is performed in three ways: (i) introducing a guidance signal that consists of the medical words in the input sequence, (ii) leveraging semantic type knowledge in UMLS to create clinically meaningful input embeddings, and (iii) making use of a novel weighted loss function that provides a stronger incentive for the model to correctly predict words with a medical meaning. By applying these three strategies, MedicalSum takes clinical knowledge into consideration during the summarization process and achieves state-of-the-art ROUGE score improvements of 0.8-2.1 points (including 6.2% ROUGE-1 error reduction in the PE section) when producing medical summaries of patient-doctor conversations.

We discuss automatic creation of medical reports from ASR-generated patient-doctor conversational transcripts using an end-to-end neural summarization approach. We explore both recurrent neural network (RNN) and Transformer-based sequence-to-sequence architectures for summarizing medical conversations. We have incorporated enhancements to these architectures, such as the pointer-generator network that facilitates copying parts of the conversations to the reports, and a hierarchical RNN encoder that makes RNN training three times faster with long inputs. A comparison of the relative improvements from the different model architectures over an oracle extractive baseline is provided on a dataset of 800k orthopedic encounters. Consistent with observations in literature for machine translation and related tasks, we find the Transformer models outperform RNN in accuracy, while taking less than half the time to train. Significantly large wins over a strong oracle baseline indicate that sequence-to-sequence modeling is a promising approach for automatic generation of medical reports, in the presence of data at scale.