# Knowledge Grounded Conversational Symptom Detection with Graph Memory Networks

arXiv: https://arxiv.org/abs/2101.09773 · HTML: https://mlsanity.com/paper/2101.09773/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 24 references that are in the map.
- First posted on arXiv 24 Jan 2021.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Neural Turing Machines](https://mlsanity.com/paper/1410.5401/) (2014)
- [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://mlsanity.com/paper/1604.04562/) (2016)
- [Key-Value Memory Networks for Directly Reading Documents](https://mlsanity.com/paper/1606.03126/) (2016)
- [GuessWhat?! Visual object discovery through multi-modal dialogue](https://mlsanity.com/paper/1611.08481/) (2016)
- [Learning End-to-End Goal-Oriented Dialog](https://mlsanity.com/paper/1605.07683/) (2016)
- [GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination](https://mlsanity.com/paper/1809.01852/) (2018)
- [End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis](https://mlsanity.com/paper/1901.10623/) (2019)
- [Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems](https://mlsanity.com/paper/1511.06931/) (2015)
- [Evaluating Visual Conversational Agents via Cooperative Human-AI Games](https://mlsanity.com/paper/1708.05122/) (2017)
- [Automatic Stance Detection Using End-to-End Memory Networks](https://mlsanity.com/paper/1804.07581/) (2018)
- [Learning Robust Dialog Policies in Noisy Environments](https://mlsanity.com/paper/1712.04034/) (2017)
- [Graph Memory Networks for Molecular Activity Prediction](https://mlsanity.com/paper/1801.02622/) (2018)
- [Prototypical Q Networks for Automatic Conversational Diagnosis and Few-Shot New Disease Adaption](https://mlsanity.com/paper/2005.11153/) (2020)

## Built on by (most cited first, 6 of 6)

- [DDXPlus: A New Dataset For Automatic Medical Diagnosis](https://mlsanity.com/paper/2205.09148/) (2022)
- [Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors' Reasoning with Deep Reinforcement Learning](https://mlsanity.com/paper/2210.07198/) (2022)
- [DDxT: Deep Generative Transformer Models for Differential Diagnosis](https://mlsanity.com/paper/2312.01242/) (2023)
- [Automatic Differential Diagnosis using Transformer-Based Multi-Label Sequence Classification](https://mlsanity.com/paper/2408.15827/) (2024)
- [LLM-Driven Medical Document Analysis: Enhancing Trustworthy Pathology and Differential Diagnosis](https://mlsanity.com/paper/2506.19702/) (2025)
- [Beyond Information Seeking: Severity-Aware Question Supervision for Proactive Medical Dialogue](https://mlsanity.com/paper/2608.24521/) (2026)

## Abstract (arXiv)

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.