Fast Prototyping a Dialogue Comprehension System for Nurse-Patient Conversations on Symptom Monitoring (N19-2)
Copied to clipboard
Zhengyuan Liu, Hazel Lim, Nur Farah Ain Suhaimi, Shao Chuen Tong, Sharon Ong, Angela Ng, Sheldon Lee, Michael R. Macdonald, Savitha Ramasamy, Pavitra Krishnaswamy, Wai Leng Chow, Nancy F. Chen
| Challenge: | a limited amount of data exists for human-human spoken dialogues for research and development . a dialogue comprehension system that extracts clinical information from spoken conversations is clinically useful . |
| Approach: | They propose a framework inspired by nurse-initiated clinical symptom monitoring conversations to construct a simulated human-human dialogue dataset. |
| Outcome: | The proposed system achieves more than 80% F1 on held-out test set from nurse-to-patient conversations. |
Similar Papers
Enhancing Dialogue Symptom Diagnosis with Global Attention and Symptom Graph (D19-1)
Copied to clipboard
| Challenge: | Existing studies on symptom diagnosis based on EHRs focus on the standard electronic medical records, but the dialogues between doctors and patients that contain more rich information are not well studied. |
| Approach: | They propose to build a global attention mechanism to capture more symptom related information and build symptom graphs to model the associations between symptoms rather than treating each symptom independently. |
| Outcome: | The proposed model achieves the state-of-the-art on the constructed dataset. |
Extracting Symptoms and their Status from Clinical Conversations (P19-1)
Copied to clipboard
| Challenge: | Existing models for extracting symptoms from clinical conversations are inherently difficult. |
| Approach: | They propose two new deep learning models tailored for a new application . they propose a hierarchical span-attribute tagging model and a sequence-to-sequence model . |
| Outcome: | The proposed models perform well under different conditions and are compared to existing models. |
Extracting relevant information from physician-patient dialogues for automated clinical note taking (D19-62)
Copied to clipboard
| Challenge: | a system that extracts pertinent medical information from dialogues between clinicians and patients is proposed . entering data into EMRs is currently slow and error-prone, and clinicians spend up to 50% of their time on data entry. |
| Approach: | They propose a system that automatically extracts medical information from dialogues between clinicians and patients using context and time information. |
| Outcome: | The proposed system extracts medical information from dialogues and automatically generates a patient note. |
Reading Turn by Turn: Hierarchical Attention Architecture for Spoken Dialogue Comprehension (P19-1)
Copied to clipboard
| Challenge: | Existing research on multi-turn spoken conversations focuses on reading comprehension of passages . interactivity of spoken content can cause lower information density and topic diffusion . |
| Approach: | They propose a hierarchical attention neural network architecture to improve spoken dialogue comprehension by combining turn-level and word-level attention mechanisms. |
| Outcome: | The proposed approach outperforms baseline attention models and is robust to lengthy and out-of-distribution test samples. |
Task-oriented Dialogue System for Automatic Diagnosis (P18-2)
Copied to clipboard
Zhongyu Wei, Qianlong Liu, Baolin Peng, Huaixiao Tou, Ting Chen, Xuanjing Huang, Kam-fai Wong, Xiangying Dai
| Challenge: | Existing methods to identify phenotypes using electronic health records (EHRs) are expensive and difficult to transfer models from one disease to another. |
| Approach: | They propose a task-oriented dialogue system framework to make diagnosis for patients automatically, which can converse with patients to collect additional symptoms beyond their self-reports. |
| Outcome: | The proposed system can collect additional symptoms from conversation and improve disease identification accuracy. |
KNSE: A Knowledge-aware Natural Language Inference Framework for Dialogue Symptom Status Recognition (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing frameworks for symptom status recognition in doctor-patient dialogues are inadequate. |
| Approach: | They propose a framework for symptom status recognition that formalizes a natural language inference task . they generate knowledge about the symptom and a hypothesis about its status for each symptom . |
| Outcome: | The proposed framework outperforms baselines and has advantages in cross-disease and cross-symptom scenarios. |
MedNgage: A Dataset for Understanding Engagement in Patient-Nurse Conversations (2023.findings-acl)
Copied to clipboard
| Challenge: | Literature suggests that actively engaged patients are more likely to obtain the full benefits of an intervention and exhibit better outcomes. |
| Approach: | They propose to annotate a dataset of patient-nurse conversations about cancer symptom management using a new framework for patient engagement. |
| Outcome: | The proposed model predicts patient-nurse conversations from socio-affective and cognitive dimensions. |
README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP (2024.findings-emnlp)
Copied to clipboard
Zonghai Yao, Nandyala Siddharth Kantu, Guanghao Wei, Hieu Tran, Zhangqi Duan, Sunjae Kwon, Zhichao Yang, Hong Yu
| Challenge: | a new task is to generate lay definitions of medical terms in EHRs that are difficult to understand for patients. |
| Approach: | They propose a task of automatically generating lay definitions to simplify medical terms into patient-friendly lay language. |
| Outcome: | The proposed model can match or surpass state-of-the-art closed-source large language models like ChatGPT with high-quality data. |
Towards Comprehensive Language Analysis for Clinically Enriched Spontaneous Dialogue (2024.lrec-main)
Copied to clipboard
| Challenge: | Contemporary NLP has progressed from feature-based classification to fine-tuning and prompt-based techniques . many of these techniques remain understudied in the context of real-world, clinically enriched spontaneous dialogue. |
| Approach: | They investigate the efficacy and overall performance of a range of NLP techniques on transcribed speech from patients with schizophrenia and other disorders. |
| Outcome: | The proposed methods are effective in analyzing transcribed speech from patients with schizophrenia and healthy controls taking a clinically-validated language test. |
Dial HEALTHDIAL for Advice: A Multilingual and Multi-Parallel Spoken Dialogue Dataset for Knowledge-Grounded Information Seeking (2026.findings-acl)
Copied to clipboard
Songbo Hu, Yinhong Liu, Ej Zhou, Evgeniia Razumovskaia, Xiaobin Wang, Alexander Fraser, Ivan Vulić, Anna Korhonen
| Challenge: | Creating spoken dialogue datasets is methodologically challenging due to the personally identifiable nature of speech signals. |
| Approach: | They propose a large-scale, multilingual, and multi-parallel dataset for developing and evaluating retrieval-augmented generation-based spoken dialogue systems. |
| Outcome: | The proposed dataset includes 6,000 information-seeking dialogues and 163 hours of user speech recorded from native speakers of four official WHO languages. |