A Two-Stage Proactive Dialogue Generator for Efficient Clinical Information Collection Using Large Language Model

• URL: http://arxiv.org/abs/2410.03770v1
• Date: Wed, 2 Oct 2024 19:32:11 GMT
• Title: A Two-Stage Proactive Dialogue Generator for Efficient Clinical Information Collection Using Large Language Model
• Authors: Xueshen Li, Xinlong Hou, Nirupama Ravi, Ziyi Huang, Yu Gan,
• Abstract summary: We propose a diagnostic dialogue system to automate the patient information collection procedure. By exploiting medical history and conversation logic, our conversation agents can pose multi-round clinical queries. Our experimental results on a real-world medical conversation dataset show that our model can generate clinical queries that mimic the conversation style of real doctors.
• Score: 0.6926413609535759
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficient patient-doctor interaction is among the key factors for a successful disease diagnosis. During the conversation, the doctor could query complementary diagnostic information, such as the patient's symptoms, previous surgery, and other related information that goes beyond medical evidence data (test results) to enhance disease diagnosis. However, this procedure is usually time-consuming and less-efficient, which can be potentially optimized through computer-assisted systems. As such, we propose a diagnostic dialogue system to automate the patient information collection procedure. By exploiting medical history and conversation logic, our conversation agents, particularly the doctor agent, can pose multi-round clinical queries to effectively collect the most relevant disease diagnostic information. Moreover, benefiting from our two-stage recommendation structure, carefully designed ranking criteria, and interactive patient agent, our model is able to overcome the under-exploration and non-flexible challenges in dialogue generation. Our experimental results on a real-world medical conversation dataset show that our model can generate clinical queries that mimic the conversation style of real doctors, with efficient fluency, professionalism, and safety, while effectively collecting relevant disease diagnostic information.

Related papers

• RuleAlign: Making Large Language Models Better Physicians with Diagnostic Rule Alignment [54.91736546490813]
  We introduce the RuleAlign framework, designed to align Large Language Models with specific diagnostic rules. We develop a medical dialogue dataset comprising rule-based communications between patients and physicians. Experimental results demonstrate the effectiveness of the proposed approach.
  arXiv  Detail & Related papers  (2024-08-22T17:44:40Z)
• Reasoning Like a Doctor: Improving Medical Dialogue Systems via Diagnostic Reasoning Process Alignment [14.563188427409958]
  Our work aims to build a medical dialogue system that aligns with clinicians' diagnostic reasoning processes. We propose a novel framework, Emulation, designed to generate an appropriate response. Our framework furnishes clear explanations for the generated responses, enhancing its transparency in medical consultations.
  arXiv  Detail & Related papers  (2024-06-20T02:02:53Z)
• Towards Knowledge-Infused Automated Disease Diagnosis Assistant [14.150224660741939]
  We build a diagnosis assistant to assist doctors, which identifies diseases based on patient-doctor interaction. We propose a two-channel, discourse-aware disease diagnosis model (KI-DDI), where the first channel encodes patient-doctor communication. In the next stage, the conversation and knowledge graph embeddings are infused together and fed to a deep neural network for disease identification.
  arXiv  Detail & Related papers  (2024-05-18T05:18:50Z)
• 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)
• Are Generative AI systems Capable of Supporting Information Needs of Patients? [4.485098382568721]
  We investigate whether and how generative visual question answering systems can responsibly support patient information needs in the context of radiology imaging data. We conducted a formative need-finding study in which participants discussed chest computed tomography (CT) scans and associated radiology reports of a fictitious close relative with a cardiothoracic radiologist. Using thematic analysis of the conversation between participants and medical experts, we identified commonly occurring themes across interactions. We evaluate two state-of-the-art generative visual language models against the radiologist's responses.
  arXiv  Detail & Related papers  (2024-01-31T23:24:37Z)
• Medical Dialogue Generation via Intuitive-then-Analytical Differential Diagnosis [14.17497921394565]
  We propose a medical dialogue generation framework with the Intuitive-then-Analytic Differential Diagnosis (IADDx) Our method starts with a differential diagnosis via retrieval-based intuitive association and subsequently refines it through a graph-enhanced analytic procedure. Experimental results on two datasets validate the efficacy of our method.
  arXiv  Detail & Related papers  (2024-01-12T12:35:19Z)
• 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)
• A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasets [70.32630628211803]
  We propose two frameworks to support automatic medical consultation, namely doctor-patient dialogue understanding and task-oriented interaction. A new large medical dialogue dataset with multi-level fine-grained annotations is introduced. We report a set of benchmark results for each task, which shows the usability of the dataset and sets a baseline for future studies.
  arXiv  Detail & Related papers  (2022-04-19T16:43:21Z)
• VBridge: Connecting the Dots Between Features, Explanations, and Data for Healthcare Models [85.4333256782337]
  VBridge is a visual analytics tool that seamlessly incorporates machine learning explanations into clinicians' decision-making workflow. We identified three key challenges, including clinicians' unfamiliarity with ML features, lack of contextual information, and the need for cohort-level evidence. We demonstrated the effectiveness of VBridge through two case studies and expert interviews with four clinicians.
  arXiv  Detail & Related papers  (2021-08-04T17:34:13Z)
• 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)
• Hierarchical Reinforcement Learning for Automatic Disease Diagnosis [52.111516253474285]
  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.
  arXiv  Detail & Related papers  (2020-04-29T15:02:41Z)

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.