M edical S um: A Guided Clinical Abstractive Summarization Model for Generating Medical Reports from Patient-Doctor Conversations (2022)

Abstract: 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.

## introduction the volume of data created in healthcare has grown considerably as a result of record keeping and regulatory requirements policies @xcite . the documentation requirements for electronic health records (ehr) have been shown to be a significant factor contributing to physician burnout @xcite . as a result, the automatic creation of medical documentation has been proposed as one way to address this issue. to date, there have been several attempts at automatically generating summaries of clinical encounters. @xcite employed a transformer model for summarizing doctor-patient conversations. @xcite developed models to summarize dialogue snippets between two to ten physician-patient turns long. finally, @xcite and @xcite utilized extractive methods to identify the most important utterances which are combined to form the final summary. the summaries generated by current summarization models are not straightforwardly controllable @xcite . dialogue summarization is also challenging because casual conversation can include interruptions, repetitions, and sudden topic transitions @xcite , and generally does not follow the structure of a written document @xcite . these challenges can lead to problems, such as the omission of key information, or the hallucination of unsupported information. summaries for medical documentation must also use the correct medical terminology expected by physicians @xcite . to help address this problem, we propose a novel knowledge-augmented transformer model that uses medical knowledge to guide the summarization process in various ways to increase the likelihood of relevant medical facts being included in the summarized output (an example of such output is in figure 1 ). key paper contributions include: (i) we are the first, to the best of our knowledge, to pro-pose the usage of medical knowledge from a clinical metathesaurus (umls @xcite ) in the summarization process of a transformer-based model in order to generate 'medically focused' clinical note summaries. (ii) we answer the question of how to incorporate structured medical knowledge in medical documentation generation by designing 3 specific signals over medical entities. (iii) by leveraging these methods the medicalsum model achieves rouge-1 and rouge-l improvement between 0.8% and 2.1% in all experiments on medical note summarization. ## related work there are two main approaches for summarization. extractive methods @xcite where the summary is created from passages that are copied from the source text and abstractive @xcite methods where phrases and words not in the source text can be used to create the summary. ## dataset for the training of the medicalsum model, we have to select a large enough dataset that would provide the necessary data for the medical signals to meaningfully affect the performance of the model. however, there are no publicly available large-scale datasets for medical summarization and thus we have to use a proprietary one. we use english data consisting of recently recorded family medicine patient-doctor visits. the speaker-diarized conversation transcripts corresponding to the audio files were obtained using an automatic speech recognizer system and medical professionals created the associated clinical notes. the reports for family medicine are organized under three sections that correspond to three broad areas of a medical note: (i) history of present illness (hpi) which captures the reason for the visit. (ii) physical examination (pe) which captures findings from a physical examination. (iii) assessment and plan (ap) which captures the assessment by the doctor and the treatment plan. as the above-mentioned dataset contains patients' private medical information it cannot be made publicly available, and that is the reason that we decided to experiment with a public dataset as well to allow for a more open comparison. we tackle the third task of the mediqa 2021 challenge (ben abacha et al., 2019) on summarization of radiology reports (rad) @xcite . from ## conclusion and future work in this paper, we present medicalsum, a novel approach for medical summarization. medicalsum can provide external medical guidance that helps key information pass the model's decision process and appear in the summary. furthermore, its novel weighted loss function provides a stronger incentive to the model to correctly predict words with a medical meaning. medicalsum can also create more meaningful input embeddings by forcing the embeddings of the words that are associated with the same semantic type to become more similar. our analysis shows that these features allowed medicalsum to produce more accurate aigenerated medical documentation. future work includes examining additional guidance signals (e.g., relational triples), and exploring umls hierarchical associations. this work is the first to show how external medical domain (umls) knowledge can effectively improve the performance of a medical note-generation model. leveraging external knowledge may become an important component of scaling and improving future medical ai systems that automatically generate medical documentation to combat physician burnout and improve patient care.