MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System

• URL: http://arxiv.org/abs/2111.09381v1
• Date: Wed, 17 Nov 2021 20:31:16 GMT
• Title: MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System
• Authors: Rhys Compton, Ilya Valmianski, Li Deng, Costa Huang, Namit Katariya, Xavier Amatriain, Anitha Kannan
• Abstract summary: We present MEDCOD, a Medically-Accurate, Emotive, Diverse, and Controllable Dialog system. It integrates medical domain knowledge with modern deep learning techniques to generate flexible, human-like natural language expressions.
• Score: 6.902975908146047
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 would communicate to the patient. Second, the generated sentence structures and phrasings are varied and diverse while maintaining medical consistency with the desired medical concept (provided by the dialogue manager module of MEDCOD). Experimental results demonstrate the effectiveness of our approach in creating a human-like medical dialogue system. Relevant code is available at https://github.com/curai/curai-research/tree/main/MEDCOD

Related papers

• Medical Vision-Language Pre-Training for Brain Abnormalities [96.1408455065347]
  We show how to automatically collect medical image-text aligned data for pretraining from public resources such as PubMed. In particular, we present a pipeline that streamlines the pre-training process by initially collecting a large brain image-text dataset. We also investigate the unique challenge of mapping subfigures to subcaptions in the medical domain.
  arXiv  Detail & Related papers  (2024-04-27T05:03:42Z)
• RaDialog: A Large Vision-Language Model for Radiology Report Generation and Conversational Assistance [53.20640629352422]
  Conversational AI tools can generate and discuss clinically correct radiology reports for a given medical image. RaDialog is the first thoroughly evaluated and publicly available large vision-language model for radiology report generation and interactive dialog. Our method achieves state-of-the-art clinical correctness in report generation and shows impressive abilities in interactive tasks such as correcting reports and answering questions.
  arXiv  Detail & Related papers  (2023-11-30T16:28:40Z)
• Language Generation from Brain Recordings [68.97414452707103]
  We propose a generative language BCI that utilizes the capacity of a large language model and a semantic brain decoder. The proposed model can generate coherent language sequences aligned with the semantic content of visual or auditory language stimuli. Our findings demonstrate the potential and feasibility of employing BCIs in direct language generation.
  arXiv  Detail & Related papers  (2023-11-16T13:37:21Z)
• Dynamic Causal Disentanglement Model for Dialogue Emotion Detection [77.96255121683011]
  We propose a Dynamic Causal Disentanglement Model based on hidden variable separation. This model effectively decomposes the content of dialogues and investigates the temporal accumulation of emotions. Specifically, we propose a dynamic temporal disentanglement model to infer the propagation of utterances and hidden variables.
  arXiv  Detail & Related papers  (2023-09-13T12:58:09Z)
• Medical Dialogue Generation via Dual Flow Modeling [9.328694317877169]
  Medical dialogue systems (MDS) aim to provide patients with medical services, such as diagnosis and prescription. Previous studies mainly addressed this by extracting the mentioned medical entities as critical dialogue history information. In this work, we argue that it is also essential to capture the transitions of the medical entities and the doctor's dialogue acts in each turn.
  arXiv  Detail & Related papers  (2023-05-29T14:23:34Z)
• CDialog: A Multi-turn Covid-19 Conversation Dataset for Entity-Aware Dialog Generation [18.047064216849204]
  We release a high-quality multi-turn Medical Dialog dataset relating to Covid-19 disease named CDialog. We propose a novel neural medical dialog system based on the CDialog dataset to advance future research on developing automated medical dialog systems.
  arXiv  Detail & Related papers  (2022-11-16T11:07:34Z)
• Terminology-aware Medical Dialogue Generation [23.54754465832362]
  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. We propose a novel framework to improve medical dialogue generation by considering features centered on domain-specific terminology.
  arXiv  Detail & Related papers  (2022-10-27T15:41:46Z)
• Prompt-based Generative Approach towards Multi-Hierarchical Medical Dialogue State Tracking [5.586690662128686]
  The dialogue state tracking (DST) module in the medical dialogue system interprets utterances into a machine-readable structure for downstream tasks. We first define a multi-hierarchical state structure and then propose a Prompt-based Generative Approach. Our approach outperforms other DST methods and is rather effective in the scenario with little data.
  arXiv  Detail & Related papers  (2022-03-18T13:28:27Z)
• Semi-Supervised Variational Reasoning for Medical Dialogue Generation [70.838542865384]
  Two key characteristics are relevant for medical dialogue generation: patient states and physician actions. We propose an end-to-end variational reasoning approach to medical dialogue generation. A physician policy network composed of an action-classifier and two reasoning detectors is proposed for augmented reasoning ability.
  arXiv  Detail & Related papers  (2021-05-13T04:14:35Z)
• MedDG: An Entity-Centric Medical Consultation Dataset for Entity-Aware Medical Dialogue Generation [86.38736781043109]
  We build and release a large-scale high-quality Medical Dialogue dataset related to 12 types of common Gastrointestinal diseases named MedDG. We propose two kinds of medical dialogue tasks based on MedDG dataset. One is the next entity prediction and the other is the doctor response generation. Experimental results show that the pre-train language models and other baselines struggle on both tasks with poor performance in our dataset.
  arXiv  Detail & Related papers  (2020-10-15T03:34:33Z)
• Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures [4.3012765978447565]
  We present a novel approach to medical conversation summarization that leverages the unique and independent local structures created when gathering a patient's medical history. Our approach is preferred on twice the number of pointers to the baseline pointer generator model and captures most or all of the information in 80% of the conversations.
  arXiv  Detail & Related papers  (2020-09-18T07:35:44Z)

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.