MedNgage: A Dataset for Understanding Engagement in Patient-Nurse Conversations (2023.findings-acl)
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| 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. |
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| Challenge: | NoteChat is a cooperative multi-agent framework for generating patient-physician dialogues . evaluator finds it outperforms state-of-the-art models for generating clinical notes . clinical documentation is largely done by physicians at both steps . |
| Approach: | They propose a cooperative multi-agent framework leveraging Large Language Models to generate patient-physician dialogues. |
| Outcome: | The proposed framework outperforms state-of-the-art models for generating clinical notes . it can engage patients directly and help clinical documentation, a leading cause of physician burnout . |
“What’s Up, Doc?”: Analyzing How Users Seek Health Information in Large-Scale Conversational AI Datasets (2025.findings-emnlp)
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Akshay Paruchuri, Maryam Aziz, Rohit Vartak, Ayman Ali, Best Uchehara, Xin Liu, Ishan Chatterjee, Monica Agrawal
| Challenge: | a growing number of people are seeking healthcare information from large language models via chatbots, yet the nature and inherent risks of these interactions remain unexplored. |
| Approach: | They use a curated dataset of 11K real-world conversations composed of 25K user messages to analyze user interactions across 21 health specialties. |
| Outcome: | The proposed dataset consists of 11K real-world conversations composed of 25K user messages. |
AcnEmpathize: A Dataset for Understanding Empathy in Dermatology Conversations (2024.lrec-main)
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| Challenge: | Existing studies on empathy and mental health-related corpora focus on broader contexts and lack domain specificity. |
| Approach: | They propose a dataset that captures empathy expressed in acne-related discussions from forum posts focused on its emotional and psychological effects. |
| Outcome: | The AcnEmpathize dataset shows that it performs well at empathy classification. |
Extracting Symptoms and their Status from Clinical Conversations (P19-1)
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| 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. |
MIMICause: Representation and automatic extraction of causal relation types from clinical notes (2022.findings-acl)
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| Challenge: | Extracted causal information from clinical notes can be combined with structured EHR data such as demographics, diagnoses, and medications. |
| Approach: | They propose to annotate clinical notes and develop an annotated corpus and provide baseline scores to identify types and direction of causal relations between a pair of biomedical concepts. |
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MedCPI: A Construct–Personalize–Integrate Framework for KG-enhanced Clinical Prediction (2026.findings-acl)
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| Challenge: | Existing KG-enhanced approaches to clinical prediction are limited . existing approaches to personalize and integrate knowledge are weakly controlled . |
| Approach: | They propose a framework to integrate medical knowledge graphs into EHRs to support KG-enhanced clinical prediction. |
| Outcome: | The proposed framework improves on MIMIC-III and MIMIC IV tasks. |
Fast Prototyping a Dialogue Comprehension System for Nurse-Patient Conversations on Symptom Monitoring (N19-2)
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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
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A Framework for Flexible Extraction of Clinical Event Contextual Properties from Electronic Health Records (2025.acl-industry)
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| Challenge: | EHRs contain vast amounts of valuable clinical data, stored as unstructured text. |
| Approach: | They propose a method that uses existing NER+L methods to classify medical entities at scale using a named entity recognition and linking task. |
| Outcome: | The proposed model outperforms Bi-LSTM in minority class tasks with up to 28% of the time and 32% faster training time. |
Joint Dialogue Topic Segmentation and Categorization: A Case Study on Clinical Spoken Conversations (2023.emnlp-industry)
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| Challenge: | Utilizing natural language processing in clinical conversations is effective to improve the efficiency of workflows for medical staff and patients. |
| Approach: | They propose a model for dialogue segmentation and topic categorization that integrates natural language processing techniques into a joint model. |
| Outcome: | The proposed model improves on follow-up calls for diabetes management and reduces computational complexity and cost. |
Listening to Patients: Detecting and Mitigating Patient Misreport in Medical Dialogue System (2025.findings-acl)
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| Challenge: | Medical Dialogue Systems (MDSs) aim to provide automated healthcare support through natural language interactions between patients and system agents. |
| Approach: | They propose a framework that detects misreports and mitigates them by generating controlled clarifying questions. |
| Outcome: | The proposed framework can detect misreports and mitigate them through generating controlled clarifying questions. |