  With the advancement of internet communication and telemedicine, people are
increasingly turning to the web for various healthcare activities. With an
ever-increasing number of diseases and symptoms, diagnosing patients becomes
challenging. In this work, we build a diagnosis assistant to assist doctors,
which identifies diseases based on patient-doctor interaction. During
diagnosis, doctors utilize both symptomatology knowledge and diagnostic
experience to identify diseases accurately and efficiently. Inspired by this,
we investigate the role of medical knowledge in disease diagnosis through
doctor-patient interaction. We propose a two-channel, knowledge-infused,
discourse-aware disease diagnosis model (KI-DDI), where the first channel
encodes patient-doctor communication using a transformer-based encoder, while
the other creates an embedding of symptom-disease using a graph attention
network (GAT). In the next stage, the conversation and knowledge graph
embeddings are infused together and fed to a deep neural network for disease
identification. Furthermore, we first develop an empathetic conversational
medical corpus comprising conversations between patients and doctors, annotated
with intent and symptoms information. The proposed model demonstrates a
significant improvement over the existing state-of-the-art models, establishing
the crucial roles of (a) a doctor's effort for additional symptom extraction
(in addition to patient self-report) and (b) infusing medical knowledge in
identifying diseases effectively. Many times, patients also show their medical
conditions, which acts as crucial evidence in diagnosis. Therefore, integrating
visual sensory information would represent an effective avenue for enhancing
the capabilities of diagnostic assistants.
