Hierarchical Reinforcement Learning for Automatic Disease Diagnosis

• URL: http://arxiv.org/abs/2004.14254v2
• Date: Tue, 7 Nov 2023 15:10:14 GMT
• Title: Hierarchical Reinforcement Learning for Automatic Disease Diagnosis
• Authors: Cheng Zhong, Kangenbei Liao, Wei Chen, Qianlong Liu, Baolin Peng, Xuanjing Huang, Jiajie Peng and Zhongyu Wei
• Abstract summary: We propose to integrate a hierarchical policy structure of two levels into the dialogue systemfor policy learning. The proposed policy structure is capable to deal with diagnosis problem including large number of diseases and symptoms.
• Score: 52.111516253474285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Motivation: Disease diagnosis oriented dialogue system models the interactive consultation procedure as Markov Decision Process and reinforcement learning algorithms are used to solve the problem. Existing approaches usually employ a flat policy structure that treat all symptoms and diseases equally for action making. This strategy works well in the simple scenario when the action space is small, however, its efficiency will be challenged in the real environment. Inspired by the offline consultation process, we propose to integrate a hierarchical policy structure of two levels into the dialogue systemfor policy learning. The high-level policy consists of amastermodel that is responsible for triggering a low-levelmodel, the lowlevel policy consists of several symptom checkers and a disease classifier. The proposed policy structure is capable to deal with diagnosis problem including large number of diseases and symptoms. Results: Experimental results on three real-world datasets and a synthetic dataset demonstrate that our hierarchical framework achieves higher accuracy and symptom recall in disease diagnosis compared with existing systems. We construct a benchmark including datasets and implementation of existing algorithms to encourage follow-up researches. Availability: The code and data is available from https://github.com/FudanDISC/DISCOpen-MedBox-DialoDiagnosis Contact: 21210980124@m.fudan.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Related papers

• Conversational Disease Diagnosis via External Planner-Controlled Large Language Models [18.93345199841588]
  This study presents a LLM-based diagnostic system that enhances planning capabilities by emulating doctors. By utilizing real patient electronic medical record data, we constructed simulated dialogues between virtual patients and doctors.
  arXiv  Detail & Related papers  (2024-04-04T06:16:35Z)
• OpenClinicalAI: An Open and Dynamic Model for Alzheimer's Disease Diagnosis [11.775648630734949]
  Alzheimer's disease (AD) cannot be reversed or cured, but timely diagnosis can significantly reduce the burden of treatment and care. Current research on AD diagnosis models usually regards the diagnosis task as a typical classification task. We propose OpenClinicalAI for direct AD diagnosis in complex and uncertain clinical settings.
  arXiv  Detail & Related papers  (2023-07-03T12:35:03Z)
• Federated Offline Reinforcement Learning [55.326673977320574]
  We propose a multi-site Markov decision process model that allows for both homogeneous and heterogeneous effects across sites. We design the first federated policy optimization algorithm for offline RL with sample complexity. We give a theoretical guarantee for the proposed algorithm, where the suboptimality for the learned policies is comparable to the rate as if data is not distributed.
  arXiv  Detail & Related papers  (2022-06-11T18:03:26Z)
• Scalable Online Disease Diagnosis via Multi-Model-Fused Actor-Critic Reinforcement Learning [9.274138493400436]
  For those seeking healthcare advice online, AI based dialogue agents capable of interacting with patients to perform automatic disease diagnosis are a viable option. This can be formulated as a problem of sequential feature (symptom) selection and classification for which reinforcement learning (RL) approaches have been proposed as a natural solution. We propose a Multi-Model-Fused Actor-Critic (MMF-AC) RL framework that consists of a generative actor network and a diagnostic critic network.
  arXiv  Detail & Related papers  (2022-06-08T03:06:16Z)
• NeuralSympCheck: A Symptom Checking and Disease Diagnostic Neural Model with Logic Regularization [59.15047491202254]
  symptom checking systems inquire users for their symptoms and perform a rapid and affordable medical assessment of their condition. We propose a new approach based on the supervised learning of neural models with logic regularization. Our experiments show that the proposed approach outperforms the best existing methods in the accuracy of diagnosis when the number of diagnoses and symptoms is large.
  arXiv  Detail & Related papers  (2022-06-02T07:57:17Z)
• DxFormer: A Decoupled Automatic Diagnostic System Based on Decoder-Encoder Transformer with Dense Symptom Representations [26.337392652262103]
  A diagnosis-oriented dialogue system queries the patient's health condition and makes predictions about possible diseases through continuous interaction with the patient. We propose a decoupled automatic diagnostic framework DxFormer, which divides the diagnosis process into two steps: symptom inquiry and disease diagnosis. 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.
  arXiv  Detail & Related papers  (2022-05-08T01:52:42Z)
• Bayesian Structural Learning for an Improved Diagnosis of Cyber-Physical Systems [0.8379286663107844]
  This paper proposes a scalable algorithm for an automated learning of a structured diagnosis model. It offers equal performance to comparable algorithms while giving better interpretability.
  arXiv  Detail & Related papers  (2021-04-02T11:14:05Z)
• Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis [50.15205065710629]
  Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. We propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis.
  arXiv  Detail & Related papers  (2021-01-27T13:16:51Z)
• Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation [150.52617238140868]
  We propose low-resource medical dialogue generation to transfer the diagnostic experience from source diseases to target ones. We also develop a Graph-Evolving Meta-Learning framework that learns to evolve the commonsense graph for reasoning disease-symptom correlations in a new disease.
  arXiv  Detail & Related papers  (2020-12-22T13:20:23Z)
• Learning Binary Semantic Embedding for Histology Image Classification and Retrieval [56.34863511025423]
  We propose a novel method for Learning Binary Semantic Embedding (LBSE) Based on the efficient and effective embedding, classification and retrieval are performed to provide interpretable computer-assisted diagnosis for histology images. Experiments conducted on three benchmark datasets validate the superiority of LBSE under various scenarios.
  arXiv  Detail & Related papers  (2020-10-07T08:36:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.