Knowledge Grounded Conversational Symptom Detection with Graph Memory Networks

TL;DR

This paper introduces a knowledge-grounded neural system for goal-oriented dialogue that automatically detects and collects clinical symptoms, improving diagnostic accuracy and efficiency through graph memory networks.

Contribution

It presents a novel neural framework with graph memory for symptom detection, incorporating explicit and implicit symptom reasoning in medical dialogues.

Findings

Achieves 67% implicit symptom discovery rate

Outperforms baseline by 4% in accuracy

Uses knowledge graph to enhance model performance

Abstract

In this work, we propose a novel goal-oriented dialog task, automatic symptom detection. We build a system that can interact with patients through dialog to detect and collect clinical symptoms automatically, which can save a doctor's time interviewing the patient. Given a set of explicit symptoms provided by the patient to initiate a dialog for diagnosing, the system is trained to collect implicit symptoms by asking questions, in order to collect more information for making an accurate diagnosis. After getting the reply from the patient for each question, the system also decides whether current information is enough for a human doctor to make a diagnosis. To achieve this goal, we propose two neural models and a training pipeline for the multi-step reasoning task. We also build a knowledge graph as additional inputs to further improve model performance. Experiments show that our model significantly outperforms the baseline by 4%, discovering 67% of implicit symptoms on average with a limited number of questions.