Patient History Summarization on Outpatient Conversation

Among various medical practices, outpatient conversation is a process that most patients experience when seeking medical assistance. Due to patient privacy concerns, the collection of outpatient conversations and patient medical records is subject to many limitations. Furthermore, researchers studying outpatient conversations are often unable to make their datasets public. Therefore, most of the previous work used consultation conversations in online medical communities as research materials, but these consultation conversations are still quite different from outpatient conversations. We collaborated with the hospital to obtain outpatient conversations and patient medical records for the study. We use Transformer-based models for summarization of outpatient conversations. During the training process, we introduce external medical datasets to help the model learn medical knowledge. Since our proposed method performs summarization through segmented conversations, the model can handle relatively long outpatient conversations. Additionally, we use our outpatient dataset to train a writing style conversion model to mimic medical notes made by physicians. Experimental results show that the outpatient dialogue summaries generated by our method have a certain reference value.