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 tasks. We perform both automatic and human evaluation of responses generated by these models. The results show that the generated responses are promising in being doctor-like, relevant to the conversation history, and clinically informative. The data and code are available at https://github.com/UCSD-AI4H/COVID-Dialogue.
ICAR-CNR（ICAR-CNR研究院）；University of Calabria（卡拉布里亚大学）；University of Bologna（博洛尼亚大学）
Dialogue systems are widely used in AI to support timely and interactive communication with users. We propose a general-purpose dialogue system architecture that leverages computational argumentation to perform reasoning and provide consistent and explainable answers. We illustrate the system using a COVID-19 vaccine information case study.
Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs. To facilitate the research and development of medical dialogue systems, we build two large-scale medical dialogue datasets: MedDialog-EN and MedDialog-CN. MedDialog-EN is an English dataset containing 0.3 million conversations between patients and doctors and 0.5 million utterances. MedDialog-CN is an Chinese dataset containing 1.1 million conversations and 4 million utterances. To our best knowledge, MedDialog-(EN,CN) are…
Medical Dialogue Generation via Dual Flow Modeling
The Hong Kong Polytechnic University（香港理工大学）；Southern University of Science and Technology（南方科技大学）；Research Institute of Trustworthy Autonomous Systems（可信自主系统研究院）
East China University of Science and Technology（华东理工大学）；Tongji University（同济大学）
The medical dialogue system is a promising application that can provide great convenience for patients. The dialogue state tracking (DST) module in the medical dialogue system which interprets utterances into the machine-readable structure for downstream tasks is particularly challenging. Firstly, the states need to be able to represent compound entities such as symptoms with their body part or diseases with degrees of severity to provide enough information for decision support. Secondly, these named entities in the utterance might be discontinuous and scattered across sentences and speakers. …
Sun Yat-sen University（中山大学）；Hong Kong University of Science and Technology（香港科技大学）；Hong Kong Polytechnic University（香港理工大学）；Tencent Jarvis Lab（腾讯天衍实验室）
Developing conversational agents to interact with patients and provide primary clinical advice has attracted increasing attention due to its huge application potential, especially in the time of COVID-19 Pandemic. However, the training of end-to-end neural-based medical dialogue system is restricted by an insufficient quantity of medical dialogue corpus. In this work, we make the first attempt to build and release a large-scale high-quality Medical Dialogue dataset related to 12 types of common Gastrointestinal diseases named MedDG, with more than 17K conversations collected from the online he…
Shandong University（山东大学）；University of Amsterdam（阿姆斯特丹大学）；Tencent（腾讯）
Medical dialogue systems (MDSs) aim to assist doctors and patients with a range of professional medical services, i.e., diagnosis, treatment and consultation. The development of MDSs is hindered because of a lack of resources. In particular. (1) there is no dataset with large-scale medical dialogues that covers multiple medical services and contains fine-grained medical labels (i.e., intents, actions, slots, values), and (2) there is no set of established benchmarks for MDSs for multi-domain, multi-service medical dialogues. In this paper, we present ReMeDi, a set of resource for medical dialo…
Sun Yat-Sen University（中山大学）；Soochow University（苏州大学）；Dark Matter AI Inc.（暗物质人工智能公司）
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.…
A medical dialogue system is essential for healthcare service as providing primary clinical advice and diagnoses. It has been gradually adopted and practiced in medical organizations in the form of a conversational bot, largely due to the advancement of NLP. In recent years, the introduction of state-of-the-art deep learning models and transfer learning techniques like Universal Language Model Fine Tuning (ULMFiT) and Knowledge Distillation (KD) largely contributes to the performance of NLP tasks. However, some deep neural networks are poorly calibrated and wrongly estimate the uncertainty. He…
Enhancing Medical Dialogue Generation through Knowledge Refinement and Dynamic Prompt Adjustment
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学公安学院人工智能学院）；Sichuan University（四川大学）；School of Computer Science and Technology, Xinjiang University（新疆大学计算机科学与技术学院）；Tsinghua University（清华大学）；School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；Engineering Research Center of Next-Generation Intelligent Search and Recommendation, Ministry of Education（教育部下一代智能搜索与推荐工程研究中心）
The development of conversational agents to interact with patients and deliver clinical advice has attracted the interest of many researchers, particularly in light of the COVID-19 pandemic. The training of an end-to-end neural based dialog system, on the other hand, is hampered by a lack of multi-turn medical dialog corpus. We make the very first attempt to release a high-quality multi-turn Medical Dialog dataset relating to Covid-19 disease named CDialog, with over 1K conversations collected from the online medical counselling websites. We annotate each utterance of the conversation with sev…
Over the past year, research in various domains, including Natural Language Processing (NLP), has been accelerated to fight against the COVID-19 pandemic, yet such research has just started on dialogue systems. In this paper, we introduce an end-to-end dialogue system which aims to ease the isolation of people under self-quarantine. We conduct a control simulation experiment to assess the effects of the user interface, a web-based virtual agent called Nora vs. the android ERICA via a video call. The experimental results show that the android offers a more valuable user experience by giving the…
People increasingly search online for answers to their medical questions but the rate at which medical questions are asked online significantly exceeds the capacity of qualified people to answer them. This leaves many questions unanswered or inadequately answered. Many of these questions are not unique, and reliable identification of similar questions would enable more efficient and effective question answering schema. COVID-19 has only exacerbated this problem. Almost every government agency and healthcare organization has tried to meet the informational need of users by building online FAQs,…
Sun Yat-sen University（中山大学）；Salesforce Research（Salesforce研究院）；DarkMatter AI Inc.（DarkMatter人工智能公司）；Tencent Jarvis Lab（腾讯Jarvis实验室）
Human doctors with well-structured medical knowledge can diagnose a disease merely via a few conversations with patients about symptoms. In contrast, existing knowledge-grounded dialogue systems often require a large number of dialogue instances to learn as they fail to capture the correlations between different diseases and neglect the diagnostic experience shared among them. To address this issue, we propose a more natural and practical paradigm, i.e., low-resource medical dialogue generation, which can transfer the diagnostic experience from source diseases to target ones with a handful of …
Hunan University（湖南大学）；JD Technology（京东科技）；Xiangya Hospital of Central South University（中南大学湘雅医院）
Medical Dialogue Generation (MDG) is intended to build a medical dialogue system for intelligent consultation, which can communicate with patients in real-time, thereby improving the efficiency of clinical diagnosis with broad application prospects. This paper presents our proposed framework for the Chinese MDG organized by the 2021 China conference on knowledge graph and semantic computing (CCKS) competition, which requires generating context-consistent and medically meaningful responses conditioned on the dialogue history. In our framework, we propose a pipeline system composed of entity pre…
Canadian VIGOUR Centre, University of Alberta（阿尔伯塔大学加拿大VIGOUR中心）；University of Alberta（阿尔伯塔大学）；Alberta Health Services（阿尔伯塔卫生服务局）；Alberta Machine Intelligence Institute（阿尔伯塔机器智能研究所）
Pandemic outbreaks such as COVID-19 occur unexpectedly, and need immediate action due to their potential devastating consequences on global health. Point-of-care routine assessments such as electrocardiogram (ECG), can be used to develop prediction models for identifying individuals at risk. However, there is often too little clinically-annotated medical data, especially in early phases of a pandemic, to develop accurate prediction models. In such situations, historical pre-pandemic health records can be utilized to estimate a preliminary model, which can then be fine-tuned based on limited av…
COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. The efficiency of COVID-19 diagnosis has become highly significant. As deep learning and convolutional neural network (CNN) has been widely utilized and been verified in analyzing medical images, it has become a powerful tool for computer-assisted diagnosis. However, there are two most significant challenges in medical image classification with the help of deep learning and neural networks, one of them is the difficulty of acquiring enough samples, which may lead to model overfitting. Privacy concerns mainl…
We present MEDCOD, a Medically-Accurate, Emotive, Diverse, and Controllable Dialog system with a unique approach to the natural language generator module. MEDCOD has been developed and evaluated specifically for the history taking task. It integrates the advantage of a traditional modular approach to incorporate (medical) domain knowledge with modern deep learning techniques to generate flexible, human-like natural language expressions. Two key aspects of MEDCOD's natural language output are described in detail. First, the generated sentences are emotive and empathetic, similar to how a doctor…
University of Patras（帕特雷大学）；Computer Technology Institute（计算机技术研究所）
In this study, a dataset of X-Ray images from patients with common pneumonia, Covid-19, and normal incidents was utilized for the automatic detection of the Coronavirus. The aim of the study is to evaluate the performance of state-of-the-art Convolutional Neural Network architectures proposed over recent years for medical image classification. Specifically, the procedure called transfer learning was adopted. With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable target, often yielding remarkable results. The dataset utilized in this expe…
Birmingham City University（伯明翰城市大学）；Assiut University（艾斯尤特大学）
Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks (CNNs) for image recognition and classification. However, due to the limited availability of annotated medical images, the classification of medical images remains the biggest challenge in medical diagnosis. Thanks to transfer learning, an effective mechanism that can provide a promising solution by transferring knowledge from generic object recognit…
The ongoing COVID-19 pandemic has raised concerns for many regarding personal and public health implications, financial security and economic stability. Alongside many other unprecedented challenges, there are increasing concerns over social isolation and mental health. We introduce \textit{Expressive Interviewing}--an interview-style conversational system that draws on ideas from motivational interviewing and expressive writing. Expressive Interviewing seeks to encourage users to express their thoughts and feelings through writing by asking them questions about how COVID-19 has impacted their…
With the COVID-19 pandemic, there is a growing urgency for medical community to keep up with the accelerating growth in the new coronavirus-related literature. As a result, the COVID-19 Open Research Dataset Challenge has released a corpus of scholarly articles and is calling for machine learning approaches to help bridging the gap between the researchers and the rapidly growing publications. Here, we take advantage of the recent advances in pre-trained NLP models, BERT and OpenAI GPT-2, to solve this challenge by performing text summarization on this dataset. We evaluate the results using ROU…
In medical dialogue summarization, summaries must be coherent and must capture all the medically relevant information in the dialogue. However, learning effective models for summarization require large amounts of labeled data which is especially hard to obtain. We present an algorithm to create synthetic training data with an explicit focus on capturing medically relevant information. We utilize GPT-3 as the backbone of our algorithm and scale 210 human labeled examples to yield results comparable to using 6400 human labeled examples (~30x) leveraging low-shot learning and an ensemble method. …
The University of Surrey（萨里大学）；The University of Sheffield（谢菲尔德大学）
Medical dialogue generation aims to generate responses according to a history of dialogue turns between doctors and patients. Unlike open-domain dialogue generation, this requires background knowledge specific to the medical domain. Existing generative frameworks for medical dialogue generation fall short of incorporating domain-specific knowledge, especially with regard to medical terminology. In this paper, we propose a novel framework to improve medical dialogue generation by considering features centered on domain-specific terminology. We leverage an attention mechanism to incorporate term…
Rensselaer Polytechnic Institute（伦斯勒理工学院）；University of Luxembourg（卢森堡大学）
Medical dialogue information extraction is becoming an increasingly significant problem in modern medical care. It is difficult to extract key information from electronic medical records (EMRs) due to their large numbers. Previously, researchers proposed attention-based models for retrieving features from EMRs, but their limitations were reflected in their inability to recognize different categories in medical dialogues. In this paper, we propose a novel model, Expert System and Attention for Labelling (ESAL). We use mixture of experts and pre-trained BERT to retrieve the semantics of differen…
Institute of Science and Technology, Taikang Insurance Group（泰康保险集团科学技术研究院）
In this paper, we introduce MeDiaQA, a novel question answering(QA) dataset, which constructed on real online Medical Dialogues. It contains 22k multiple-choice questions annotated by human for over 11k dialogues with 120k utterances between patients and doctors, covering 150 specialties of diseases, which are collected from haodf.com and dxy.com. MeDiaQA is the first QA dataset where reasoning over medical dialogues, especially their quantitative contents. The dataset has the potential to test the computing, reasoning and understanding ability of models across multi-turn dialogues, which is c…
The First Affiliated Hospital of Jinan University（暨南大学附属第一医院）；The Fifth Affiliated Hospital of Sun Yat-sen University（中山大学附属第五医院）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；The Chinese University of Hong Kong（香港中文大学）；Future Network of Intelligence Institute (FNii)（未来智能网络研究院）；Sun Yat-sen University（中山大学）；Jinan University（暨南大学）；Guangdong Provincial Key Laboratory of Big Data Computing（广东省大数据计算重点实验室）
Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19. Since manual report writing is usually too time-consuming, a more intelligent auxiliary medical system that could generate medical reports automatically and immediately is urgently needed. In this article, we propose to use the medical visual language BERT (Medical-VLBERT) model to identify the abnormality on the COVID-19 scans and generate the medical report automatically based on the detected lesion regions. To produce more accurate medical …
Understanding a medical conversation between a patient and a physician poses a unique natural language understanding challenge since it combines elements of standard open ended conversation with very domain specific elements that require expertise and medical knowledge. Summarization of medical conversations is a particularly important aspect of medical conversation understanding since it addresses a very real need in medical practice: capturing the most important aspects of a medical encounter so that they can be used for medical decision making and subsequent follow ups. In this paper we pre…
Coronavirus disease 2019 (COVID-19) has infected more than one million individuals all over the world and caused more than 55,000 deaths, as of April 3 in 2020. Radiological findings are important sources of information in guiding the diagnosis and treatment of COVID-19. However, the existing studies on how radiological findings are correlated with COVID-19 are conducted separately by different hospitals, which may be inconsistent or even conflicting due to population bias. To address this problem, we develop natural language processing methods to analyze a large collection of COVID-19 literat…
MedSynth: Realistic, Synthetic Medical Dialogue-Note Pairs
Dalhousie University（达尔豪斯大学）；Vector Institute（向量研究所）；Shahrood University of Technology（沙霍尔德大学）；Chapman University（查普曼大学）；Cedars-Sinai Medical Center（Cedars-Sinai 医疗中心）
EmplifAI: a Fine-grained Dataset for Japanese Empathetic Medical Dialogues in 28 Emotion Labels
Kyoto Institute of Technology, Japan（京都技术大学）；National Institute of Information and Communications Technology (NICT), Japan（信息通信技术国家研究所）；Kyoto University, Japan（京都大学）；Nara Institute of Science and Technology, Japan（奈良科学技术大学）
In recent years, interest has arisen in using machine learning to improve the efficiency of automatic medical consultation and enhance patient experience. In this article, we propose two frameworks to support automatic medical consultation, namely doctor-patient dialogue understanding and task-oriented interaction. We create a new large medical dialogue dataset with multi-level finegrained annotations and establish five independent tasks, including named entity recognition, dialogue act classification, symptom label inference, medical report generation and diagnosis-oriented dialogue policy. W…
EMRModel: A Large Language Model for Extracting Medical Consultation Dialogues into Structured Medical Records
Taihe County People’s Hospital（太和县人民医院）；Innovation + Research Institute（创新+研究院）；GuoChuang Cloud Technology Ltd（国创云科技有限公司）；Xunfei Healthcare Technology Ltd（讯飞医疗科技有限公司）；University of Southern California（南加州大学）；The First Affiliated Hospital of Anhui Medical University（安徽医科大学第一附属医院）
IBM Research（IBM研究院）；Johns Hopkins Bloomberg School of Public Health（约翰斯·霍普金斯大学布隆伯格公共卫生学院）；New York University（纽约大学）
The COVID-19 pandemic has made a huge global impact and cost millions of lives. As COVID-19 vaccines were rolled out, they were quickly met with widespread hesitancy. To address the concerns of hesitant people, we launched VIRA, a public dialogue system aimed at addressing questions and concerns surrounding the COVID-19 vaccines. Here, we release VIRADialogs, a dataset of over 8k dialogues conducted by actual users with VIRA, providing a unique real-world conversational dataset. In light of rapid changes in users' intents, due to updates in guidelines or in response to new information, we high…
Medical Dialogue: A Survey of Categories, Methods, Evaluation and Challenges
Shanghai AI Lab（上海人工智能实验室）；Beihang University（北京航空航天大学）；Beijing Academy of AI（北京人工智能研究院）；Zhijiang Lab（之江实验室）；The Chinese University of Hong Kong（香港中文大学）；Beijing Institute of Technology（北京理工大学）；East China University of Science and Technology（华东理工大学）
Shandong University（山东大学）；Baidu Inc.（百度公司）；University of Amsterdam（阿姆斯特丹大学）；Ahold Delhaize（阿霍德德尔海兹集团）
Medical dialogue generation aims to provide automatic and accurate responses to assist physicians to obtain diagnosis and treatment suggestions in an efficient manner. In medical dialogues two key characteristics are relevant for response generation: patient states (such as symptoms, medication) and physician actions (such as diagnosis, treatments). In medical scenarios large-scale human annotations are usually not available, due to the high costs and privacy requirements. Hence, current approaches to medical dialogue generation typically do not explicitly account for patient states and physic…
