Knowledge Grounded Conversational Symptom Detection with Graph Memory Networks
This addresses the problem of efficient clinical symptom collection for healthcare professionals, though it is incremental as it builds on existing dialog and graph-based methods.
The paper tackles the problem of automatic symptom detection through goal-oriented dialog to save doctors' time, achieving a 4% performance improvement over baselines and discovering 67% of implicit symptoms on average with limited questions.
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.