Challenge: a new system that integrates advanced NLP technologies for medical insurance assessment is proposed . the average time cost of the procedure is reduced from 55 minutes to 35 minutes .
Approach: They propose a dialogue-based information extraction system that integrates advanced NLP technologies for medical insurance assessment.
Outcome: The proposed system reduces the time cost of the procedure from 55 minutes to 35 minutes and saves 30% human resources cost compared with the previous offline procedure.

Similar Papers

MIE: A Medical Information Extractor towards Medical Dialogues (2020.acl-main)

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Challenge: EMRs are important but many doctors suffer from writing them, which is time-consuming and tedious.
Approach: They propose an automatic conversion of medical dialogues to EMRs using a window-sliding style . they propose a medical information extractor (MIE) that extracts medical information from medical dialogue .
Outcome: The proposed model extracts medical information from doctor-patient dialogues.
Extracting relevant information from physician-patient dialogues for automated clinical note taking (D19-62)

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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.
A Speaker-Aware Co-Attention Framework for Medical Dialogue Information Extraction (2022.emnlp-main)

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Challenge: With the development of medical digitization, the extraction and structuring of electronic medical records (EMRs) have become challenging but fundamental tasks.
Approach: They propose a speaker-aware dialogue encoder with multi-task learning which takes the speaker's identity into account and a co-attention fusion network to aggregate the utterance information.
Outcome: The proposed framework outperforms the state-of-the-art methods on the public medical dialogue extraction datasets to demonstrate its superiority.
COVID-19 Claim Radar: A Structured Claim Extraction and Tracking System (2022.acl-demo)

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Challenge: a new system extracts supporting and refuting claims from COVID-19 related news . the system is publicly available at GitHub and DockerHub, with complete documentation.
Approach: They propose a COVID-19 Claim Radar system that extracts supporting and refuting claims . the system leverages Wikidata as the hub to consolidate coreferential knowledge elements .
Outcome: The system extracts supporting and refuting claims from COVID-19 pandemic information . it leverages Wikidata as the hub to merge coreferential knowledge elements .
Empowering Healthcare Practitioners with Language Models: Structuring Speech Transcripts in Two Real-World Clinical Applications (2025.emnlp-industry)

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Challenge: Large language models (LLMs) have demonstrated strong performance on clinical natural language processing tasks across multiple medical benchmarks.
Approach: They propose an agentic pipeline for generating realistic, non-sensitive nurse dictations, enabling structured extraction of clinical observations.
Outcome: The proposed pipeline generates realistic, non-sensitive nurse dictations, enabling structured extraction of clinical observations.
Claim Extraction and Law Matching for COVID-19-related Legislation (2022.lrec-1)

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Challenge: Existing approaches to extract legal claims from news articles and match them with applicable laws are difficult for laypersons to learn since news articles do not refer to underlying laws.
Approach: They propose an automated approach to extract legal claims from news articles and match the claims with applicable laws.
Outcome: The proposed model achieves 46.7 F1 for claim extraction and 91.4 F1 law matching, despite conceptual limitations.
Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims (2022.naacl-main)

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Challenge: Existing datasets focus on a single medium, information domain or specific application . authors propose novel methods for automated veracity assessment based on Natural Language Inference .
Approach: They propose to build a PANACEA dataset that combines different data sources with different foci to ensure a unique set of claims.
Outcome: The proposed methods are competitive with SOTA methods and provide a detailed discussion.
On the Generation of Medical Dialogs for COVID-19 (2021.acl-short)

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Challenge: under the pandemic of COVID-19, people experiencing COVI D19-related symptoms have a pressing need to consult doctors.
Approach: They develop a medical dialog system that can provide COVID19-related consultations . they use two dialog datasets containing conversations between doctors and patients .
Outcome: The proposed system can provide COVID19-related consultations, but is too small compared with general-domain dialog datasets.
Task-oriented Dialogue System for Automatic Diagnosis (P18-2)

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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.
Towards Effective Extraction and Evaluation of Factual Claims (2025.acl-long)

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Challenge: Lack of a standardized evaluation framework impedes assessment and comparison of claim extraction methods.
Approach: They propose a framework for evaluating claim extraction in the context of fact-checking . they also introduce Claimify, an LLM-based claim extraction method .
Outcome: The proposed evaluation framework outperforms existing methods in the evaluation of claim extraction methods.

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