Heterogeneous graph reasoning for knowledge-grounded medical dialogue system

2021 ◽  
Vol 442 ◽  
pp. 260-268
Author(s):  
Wenge Liu ◽  
Jianheng Tang ◽  
Xiaodan Liang ◽  
Qingling Cai
Keyword(s):  
Dialogue System ◽  
Medical Dialogue

scholarly journals End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis

2019 ◽  
Vol 33 ◽  
pp. 7346-7353
Author(s):  
Lin Xu ◽  
Qixian Zhou ◽  
Ke Gong ◽  
Xiaodan Liang ◽  
Jianheng Tang ◽  
...  
Keyword(s):  
Natural Language ◽  
Medical Diagnosis ◽  
Expert Knowledge ◽  
Medical Knowledge ◽  
Knowledge Graph ◽  
Dialogue Systems ◽  
Dialogue System ◽  
Self Reports ◽  
Medical Dialogue ◽  
End To End

Beyond current conversational chatbots or task-oriented dialogue systems that have attracted increasing attention, we move forward to develop a dialogue system for automatic medical diagnosis that converses with patients to collect additional symptoms beyond their self-reports and automatically makes a diagnosis. Besides the challenges for conversational dialogue systems (e.g. topic transition coherency and question understanding), automatic medical diagnosis further poses more critical requirements for the dialogue rationality in the context of medical knowledge and symptom-disease relations. Existing dialogue systems (Madotto, Wu, and Fung 2018; Wei et al. 2018; Li et al. 2017) mostly rely on datadriven learning and cannot be able to encode extra expert knowledge graph. In this work, we propose an End-to-End Knowledge-routed Relational Dialogue System (KR-DS) that seamlessly incorporates rich medical knowledge graph into the topic transition in dialogue management, and makes it cooperative with natural language understanding and natural language generation. A novel Knowledge-routed Deep Q-network (KR-DQN) is introduced to manage topic transitions, which integrates a relational refinement branch for encoding relations among different symptoms and symptomdisease pairs, and a knowledge-routed graph branch for topic decision-making. Extensive experiments on a public medical dialogue dataset show our KR-DS significantly beats stateof-the-art methods (by more than 8% in diagnosis accuracy). We further show the superiority of our KR-DS on a newly collected medical dialogue system dataset, which is more challenging retaining original self-reports and conversational data between patients and doctors.

scholarly journals On the Generation of Medical Dialogues for COVID-19

2020 ◽  
Author(s):  
Wenmian Yang ◽  
Guangtao Zeng ◽  
Bowen Tan ◽  
Zeqian Ju ◽  
Subrato Chakravorty ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Transfer Learning ◽  
Large Scale ◽  
Medical Professionals ◽  
Dialogue System ◽  
Consulting Services ◽  
Medical Dialogue ◽  
Hospital Closure

Under the pandemic of COVID-19, people experiencing COVID19-related symptoms or exposed to risk factors have a pressing need to consult doctors. Due to hospital closure, a lot of consulting services have been moved online. Because of the shortage of medical professionals, many people cannot receive online consultations timely. To address this problem, we aim to develop a medical dialogue system that can provide COVID19-related consultations. We collected two dialogue datasets - CovidDialog - (in English and Chinese respectively) containing conversations between doctors and patients about COVID-19. On these two datasets, we train several dialogue generation models based on Transformer, GPT, and BERT-GPT. Since the two COVID-19 dialogue datasets are small in size, which bear high risk of overfitting, we leverage transfer learning to mitigate data deficiency. Specifically, we take the pretrained models of Transformer, GPT, and BERT-GPT on dialog datasets and other large-scale texts, then finetune them on our CovidDialog datasets. Experiments demonstrate that these approaches are promising in generating meaningful medical dialogue about COVID-19. But more advanced approaches are needed to build a fully useful dialogue system that can offer accurate COVID-related consultations. The data and code are available at https://github.com/UCSD-AI4H/COVID-Dialogue

scholarly journals On the Generation of Medical Dialogues for COVID19

2020 ◽  
Author(s):  
Wenmian Yang ◽  
Guangtao Zeng ◽  
Bowen Tan ◽  
Zeqian Ju ◽  
Subrato Chakravorty ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Transfer Learning ◽  
Large Scale ◽  
Medical Professionals ◽  
Dialogue System ◽  
Consulting Services ◽  
Medical Dialogue ◽  
Hospital Closure

<div>Under the pandemic of COVID-19, people experiencing COVID19-related symptoms or exposed to risk factors have a pressing need to consult doctors. Due to hospital closure,</div><div>a lot of consulting services have been moved online. Because of the shortage of medical professionals, many people cannot receive online consultations timely. To address this problem, we aim to develop a medical dialogue system that can provide COVID19-related consultations. We collected two dialogue datasets - CovidDialog - (in English and Chinese respectively) containing conversations between doctors and patients about COVID-19. On these two datasets, we train several dialogue generation models based on Transformer, GPT, and BERT-GPT. Since the two COVID-19 dialogue datasets are small in size, which bear high risk of overftting, we leverage transfer learning to mitigate data deficiency. Specifically, we take the pretrained models of Transformer, GPT, and BERT-GPT on dialog datasets and other large-scale texts, then finetune them on our CovidDialog datasets. Experiments demonstrate that these approaches are promising in generating meaningful medical dialogues about COVID-19. But more advanced approaches are needed to build a fully useful dialogue system that can offer accurate COVID-related consultations. The data and code are available at https://github.com/UCSD-AI4H/COVID-Dialogue</div>

scholarly journals On the Generation of Medical Dialogues for COVID19

2020 ◽  
Author(s):  
Wenmian Yang ◽  
Guangtao Zeng ◽  
Bowen Tan ◽  
Zeqian Ju ◽  
Subrato Chakravorty ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Transfer Learning ◽  
Large Scale ◽  
Medical Professionals ◽  
Dialogue System ◽  
Consulting Services ◽  
Medical Dialogue ◽  
Hospital Closure

<div>Under the pandemic of COVID-19, people experiencing COVID19-related symptoms or exposed to risk factors have a pressing need to consult doctors. Due to hospital closure,</div><div>a lot of consulting services have been moved online. Because of the shortage of medical professionals, many people cannot receive online consultations timely. To address this problem, we aim to develop a medical dialogue system that can provide COVID19-related consultations. We collected two dialogue datasets - CovidDialog - (in English and Chinese respectively) containing conversations between doctors and patients about COVID-19. On these two datasets, we train several dialogue generation models based on Transformer, GPT, and BERT-GPT. Since the two COVID-19 dialogue datasets are small in size, which bear high risk of overftting, we leverage transfer learning to mitigate data deficiency. Specifically, we take the pretrained models of Transformer, GPT, and BERT-GPT on dialog datasets and other large-scale texts, then finetune them on our CovidDialog datasets. Experiments demonstrate that these approaches are promising in generating meaningful medical dialogues about COVID-19. But more advanced approaches are needed to build a fully useful dialogue system that can offer accurate COVID-related consultations. The data and code are available at https://github.com/UCSD-AI4H/COVID-Dialogue</div>

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