4,074 research outputs found
Task-oriented Dialogue System for Automatic Disease Diagnosis via Hierarchical Reinforcement Learning
In this paper, we focus on automatic disease diagnosis with reinforcement
learning (RL) methods in task-oriented dialogues setting. Different from
conventional RL tasks, the action space for disease diagnosis (i.e., symptoms)
is inevitably large, especially when the number of diseases increases. However,
existing approaches to this problem employ a flat RL policy, which typically
works well in simple tasks but has significant challenges in complex scenarios
like disease diagnosis. Towards this end, we propose to integrate a
hierarchical policy of two levels into the dialogue policy learning. The high
level policy consists of a model named master that is responsible for
triggering a model in low level, the low level policy consists of several
symptom checkers and a disease classifier. Experimental results on both
self-constructed real-world and synthetic datasets demonstrate that our
hierarchical framework achieves higher accuracy in disease diagnosis compared
with existing systems. Besides, the datasets
(http://www.sdspeople.fudan.edu.cn/zywei/data/Fudan-Medical-Dialogue2.0) and
codes (https://github.com/nnbay/MeicalChatbot-HRL) are all available now
A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare
Reinforcement learning (RL) has emerged as a powerful approach for tackling
complex medical decision-making problems such as treatment planning,
personalized medicine, and optimizing the scheduling of surgeries and
appointments. It has gained significant attention in the field of Natural
Language Processing (NLP) due to its ability to learn optimal strategies for
tasks such as dialogue systems, machine translation, and question-answering.
This paper presents a review of the RL techniques in NLP, highlighting key
advancements, challenges, and applications in healthcare. The review begins by
visualizing a roadmap of machine learning and its applications in healthcare.
And then it explores the integration of RL with NLP tasks. We examined dialogue
systems where RL enables the learning of conversational strategies, RL-based
machine translation models, question-answering systems, text summarization, and
information extraction. Additionally, ethical considerations and biases in
RL-NLP systems are addressed
DxFormer: A Decoupled Automatic Diagnostic System Based on Decoder-Encoder Transformer with Dense Symptom Representations
Diagnosis-oriented dialogue system queries the patient's health condition and
makes predictions about possible diseases through continuous interaction with
the patient. A few studies use reinforcement learning (RL) to learn the optimal
policy from the joint action space of symptoms and diseases. However, existing
RL (or Non-RL) methods cannot achieve sufficiently good prediction accuracy,
still far from its upper limit. To address the problem, we propose a decoupled
automatic diagnostic framework DxFormer, which divides the diagnosis process
into two steps: symptom inquiry and disease diagnosis, where the transition
from symptom inquiry to disease diagnosis is explicitly determined by the
stopping criteria. In DxFormer, we treat each symptom as a token, and formalize
the symptom inquiry and disease diagnosis to a language generation model and a
sequence classification model respectively. We use the inverted version of
Transformer, i.e., the decoder-encoder structure, to learn the representation
of symptoms by jointly optimizing the reinforce reward and cross entropy loss.
Extensive experiments on three public real-world datasets prove that our
proposed model can effectively learn doctors' clinical experience and achieve
the state-of-the-art results in terms of symptom recall and diagnostic
accuracy.Comment: 7 pages, 4 figures, 3 table
Symptoms are known by their companies: towards association guided disease diagnosis assistant
Over the last few years, dozens of healthcare surveys have shown a shortage of doctors and an alarming doctor-population ratio. With the motivation of assisting doctors and utilizing their time efficiently, automatic disease diagnosis using artificial intelligence is experiencing an ever-growing demand and popularity. Humans are known by the company they keep; similarly, symptoms also exhibit the association property, i.e., one symptom may strongly suggest another symptom's existence/non-existence, and their association provides crucial information about the suffering condition. The work investigates the role of symptom association in symptom investigation and disease diagnosis process. We propose and build a virtual assistant called Association guided Symptom Investigation and Diagnosis Assistant (A-SIDA) using hierarchical reinforcement learning. The proposed A-SIDDA converses with patients and extracts signs and symptoms as per patients' chief complaints and ongoing dialogue context. We infused association-based recommendations and critic into the assistant, which reinforces the assistant for conducting context-aware, symptom-association guided symptom investigation. Following the symptom investigation, the assistant diagnoses a disease based on the extracted signs and symptoms. The assistant then diagnoses a disease based on the extracted signs and symptoms. In addition to diagnosis accuracy, the relevance of inspected symptoms is critical to the usefulness of a diagnosis framework. We also propose a novel evaluation metric called Investigation Relevance Score (IReS), which measures the relevance of symptoms inspected during symptom investigation. The obtained improvements (Diagnosis success rate-5.36%, Dialogue length-1.16, Match rate-2.19%, Disease classifier-6.36%, IReS-0.3501, and Human score-0.66) over state-of-the-art methods firmly establish the crucial role of symptom association that gets uncovered by the virtual agent. Furthermore, we found that the association guided symptom investigation greatly increases human satisfaction, owing to its seamless topic (symptom) transition
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
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