Knowledge Grounded Conversational Symptom Detection with Graph Memory Networks

Hongyin Luo, Shang-Wen Li, James Glass


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.
Anthology ID:
2020.clinicalnlp-1.16
Volume:
Proceedings of the 3rd Clinical Natural Language Processing Workshop
Month:
November
Year:
2020
Address:
Online
Venues:
ClinicalNLP | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
136–145
Language:
URL:
https://aclanthology.org/2020.clinicalnlp-1.16
DOI:
10.18653/v1/2020.clinicalnlp-1.16
Bibkey:
Cite (ACL):
Hongyin Luo, Shang-Wen Li, and James Glass. 2020. Knowledge Grounded Conversational Symptom Detection with Graph Memory Networks. In Proceedings of the 3rd Clinical Natural Language Processing Workshop, pages 136–145, Online. Association for Computational Linguistics.
Cite (Informal):
Knowledge Grounded Conversational Symptom Detection with Graph Memory Networks (Luo et al., ClinicalNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.clinicalnlp-1.16.pdf
Video:
 https://slideslive.com/38939821
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