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MediFact at MEDIQA-CORR 2024: Why AI Needs a Human Touch

Nadia Saeed

TL;DR

The paper tackles the problem of automatic single-word error correction in clinical notes, where generic LLM approaches may underperform due to domain-specific nuances and data sensitivity. It presents MediFact-CORR QA, a data-efficient two-stage framework that combines weak supervision for error detection with extractive QA for observed corrections and abstractive QA for unseen errors, anchored by domain-specific TF-IDF features and SVMs, plus a BART-based QA component for generalization. Key contributions include demonstrating competitive performance on the MEDIQA-CORR 2024 dataset, highlighting improvements in error-flag and error-sentence detection, and emphasizing a human-centric, interpretable approach over black-box LLMs. The work advances trustworthy AI in healthcare by showing how targeted information extraction and QA-driven corrections can improve the quality and safety of clinical text while mitigating data and privacy concerns associated with large LLMs.

Abstract

Accurate representation of medical information is crucial for patient safety, yet artificial intelligence (AI) systems, such as Large Language Models (LLMs), encounter challenges in error-free clinical text interpretation. This paper presents a novel approach submitted to the MEDIQA-CORR 2024 shared task (Ben Abacha et al., 2024a), focusing on the automatic correction of single-word errors in clinical notes. Unlike LLMs that rely on extensive generic data, our method emphasizes extracting contextually relevant information from available clinical text data. Leveraging an ensemble of extractive and abstractive question-answering approaches, we construct a supervised learning framework with domain-specific feature engineering. Our methodology incorporates domain expertise to enhance error correction accuracy. By integrating domain expertise and prioritizing meaningful information extraction, our approach underscores the significance of a human-centric strategy in adapting AI for healthcare.

MediFact at MEDIQA-CORR 2024: Why AI Needs a Human Touch

TL;DR

The paper tackles the problem of automatic single-word error correction in clinical notes, where generic LLM approaches may underperform due to domain-specific nuances and data sensitivity. It presents MediFact-CORR QA, a data-efficient two-stage framework that combines weak supervision for error detection with extractive QA for observed corrections and abstractive QA for unseen errors, anchored by domain-specific TF-IDF features and SVMs, plus a BART-based QA component for generalization. Key contributions include demonstrating competitive performance on the MEDIQA-CORR 2024 dataset, highlighting improvements in error-flag and error-sentence detection, and emphasizing a human-centric, interpretable approach over black-box LLMs. The work advances trustworthy AI in healthcare by showing how targeted information extraction and QA-driven corrections can improve the quality and safety of clinical text while mitigating data and privacy concerns associated with large LLMs.

Abstract

Accurate representation of medical information is crucial for patient safety, yet artificial intelligence (AI) systems, such as Large Language Models (LLMs), encounter challenges in error-free clinical text interpretation. This paper presents a novel approach submitted to the MEDIQA-CORR 2024 shared task (Ben Abacha et al., 2024a), focusing on the automatic correction of single-word errors in clinical notes. Unlike LLMs that rely on extensive generic data, our method emphasizes extracting contextually relevant information from available clinical text data. Leveraging an ensemble of extractive and abstractive question-answering approaches, we construct a supervised learning framework with domain-specific feature engineering. Our methodology incorporates domain expertise to enhance error correction accuracy. By integrating domain expertise and prioritizing meaningful information extraction, our approach underscores the significance of a human-centric strategy in adapting AI for healthcare.
Paper Structure (11 sections, 4 figures, 2 tables)

This paper contains 11 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Error Sentence Identification with Weak Supervision Motivation
  4. Error Correction with Extractive QA
  5. Error Correction with Abstractive QA
  6. Experimental Setup and Results
  7. Dataset
  8. Evaluation Metrics
  9. Results
  10. Discussion
  11. Future Work

Figures (4)

  • Figure 1: MediFact-CORR: Framework of the Correct SVM model
  • Figure 2: MediFact-CORR: Framework of the Error SVM model
  • Figure 3: MediFact-CORR: Framework of the Error Correction with Extractive QA
  • Figure 4: MediFact-CORR: Framework of the Error Correction with Abstractive QA