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Related Experiment Video

Updated: Sep 10, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Symptom Recognition in Medical Conversations Via multi- Instance Learning and Prompt.

Hua Wang1, Xue-Feng Bai1, Xiu-Tao Cui2,3

  • 1School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China.

Journal of Medical Systems
|August 20, 2025
PubMed
Summary

This study introduces a new method for extracting symptoms from medical conversations using multi-instance learning and prompt-guided attention. The approach improves accuracy in electronic health record (EHR) generation by identifying specific symptom mentions in dialogue.

Keywords:
Information extractionMedical dialogue systemsMulti-instance learningPrompt-Guided attention mechanismSymptom recognition

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Area of Science:

  • Natural Language Processing
  • Medical Informatics
  • Machine Learning

Background:

  • Electronic health record (EHR) systems require automatic symptom extraction from medical dialogues.
  • Challenges include scattered clues, informal language, and ambiguous/negated statements in patient descriptions.
  • Existing models struggle with unstructured, multi-turn conversations.

Purpose of the Study:

  • To develop a novel approach for fine-grained symptom identification in medical dialogues.
  • To improve the accuracy and efficiency of automated medical record generation.
  • To address challenges of scattered information, non-standard terminology, and negation in patient-reported symptoms.

Main Methods:

  • A multi-instance learning (MIL) framework treats conversations as bags of utterances for improved recall.
  • Prompt-guided attention leverages standardized terminology to recognize synonyms, implicit mentions, and negations, enhancing precision.
  • R-Drop regularization is used to improve robustness against noisy data.

Main Results:

  • The proposed method achieved an 85.93% F1-score, outperforming baselines by approximately 8%.
  • Achieved 85.09% precision and 86.83% recall, demonstrating strong performance.
  • Successfully identified specific symptom-utterance pairs, showcasing fine-grained extraction capabilities.

Conclusions:

  • The combined MIL and prompt-guided attention approach effectively addresses challenges in symptom extraction from medical dialogues.
  • This method enables precise symptom documentation, advancing automated medical information extraction and intelligent EHR systems.
  • The fine-grained extraction supports diagnostic decision support and improves the quality of medical records.