14 research outputs found
End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis
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 data-driven 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 symptom-disease 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
state-of-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.Comment: 8 pages, 5 figues, AAA
MKA: A Scalable Medical Knowledge Assisted Mechanism for Generative Models on Medical Conversation Tasks
Using natural language processing (NLP) technologies to develop medical
chatbots makes the diagnosis of the patient more convenient and efficient,
which is a typical application in healthcare AI. Because of its importance,
lots of research have been come out. Recently, the neural generative models
have shown their impressive ability as the core of chatbot, while it cannot
scale well when directly applied to medical conversation due to the lack of
medical-specific knowledge. To address the limitation, a scalable Medical
Knowledge Assisted mechanism, MKA, is proposed in this paper. The mechanism
aims to assist general neural generative models to achieve better performance
on the medical conversation task. The medical-specific knowledge graph is
designed within the mechanism, which contains 6 types of medical-related
information, including department, drug, check, symptom, disease, food.
Besides, the specific token concatenation policy is defined to effectively
inject medical information into the input data. Evaluation of our method is
carried out on two typical medical datasets, MedDG and MedDialog-CN. The
evaluation results demonstrate that models combined with our mechanism
outperform original methods in multiple automatic evaluation metrics. Besides,
MKA-Bert-GPT achieves state-of-the-art performance. The open-sourced codes are
public:
https://github.com/LIANGKE23/Knowledge_Assisted_Medical_Dialogue_Generation_Mechanis
Trends and Overview: The Potential of Conversational Agents in Digital Health
With the COVID-19 pandemic serving as a trigger, 2020 saw an unparalleled global expansion of tele-health [23]. Tele-health successfully lowers the need for in-person consultations and, thus, the danger of contracting a virus. While the COVID-19 pandemic sped up the adoption of virtual healthcare delivery in numerous nations, it also accelerated the creation of a wide range of other different technology-enabled systems and procedures for providing virtual healthcare to patients. Rightly so, the COVID-19 has brought many difficulties for patients (https://www.who.int/news/item/02-03-2022-covid-19-pandemic-triggers-25-increase-in-prevalence-of-anxiety-and-depression-worldwide ) who need continuing care and monitoring for mental health issues and/or other chronic diseases
Learning Health-Bots from Training Data that was Automatically Created using Paraphrase Detection and Expert Knowledge
International audienceA key bottleneck for developing dialog models is the lack of adequate training data. Due to privacy issues, dialog data is even scarcer in the health domain. We propose a novel method for creating dialog corpora which we apply to create doctor-patient interaction data. We use this data to learn both a generation and a hybrid classification/retrieval model and find that the generation model consistently outperforms the hybrid model. We show that our data creation method has several advantages. Not only does it allow for the semi-automatic creation of large quantities of training data. It also provides a natural way of guiding learning and a novel method for assessing the quality of human-machine interactions
HuatuoGPT, towards Taming Language Model to Be a Doctor
In this paper, we present HuatuoGPT, a large language model (LLM) for medical
consultation. The core recipe of HuatuoGPT is to leverage both
\textit{distilled data from ChatGPT} and \textit{real-world data from doctors}
in the supervised fine-tuned stage. The responses of ChatGPT are usually
detailed, well-presented and informative while it cannot perform like a doctor
in many aspects, e.g. for integrative diagnosis. We argue that real-world data
from doctors would be complementary to distilled data in the sense the former
could tame a distilled language model to perform like doctors. To better
leverage the strengths of both data, we train a reward model to align the
language model with the merits that both data bring, following an RLAIF
(reinforced learning from AI feedback) fashion. To evaluate and benchmark the
models, we propose a comprehensive evaluation scheme (including automatic and
manual metrics). Experimental results demonstrate that HuatuoGPT achieves
state-of-the-art results in performing medical consultation among open-source
LLMs in GPT-4 evaluation, human evaluation, and medical benchmark datasets. It
is worth noting that by using additional real-world data and RLAIF, the
distilled language model (i.e., HuatuoGPT) outperforms its teacher model
ChatGPT in most cases. Our code, data, and models are publicly available at
\url{https://github.com/FreedomIntelligence/HuatuoGPT}. The online demo is
available at \url{https://www.HuatuoGPT.cn/}