HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking

Alapfogalmak

Fact-checking in healthcare is crucial for verifying health claims using evidence-based approaches.

Kivonat

The content introduces the HealthFC dataset, focusing on fact-checking health claims. It covers dataset creation, analysis, challenges, and experiments with baseline systems. The dataset includes 750 health-related claims labeled by medical experts and backed by evidence from clinical trials. Various models were tested for evidence selection and veracity prediction tasks.

1. Introduction

   • Seeking health advice online is common.
   • Fact-checking helps assess trustworthiness.

2. Dataset Construction

   • Data sourced from Medizin Transparent.
   • Claims paired with evidence documents.

3. Descriptive Statistics of Dataset

   • Dataset consists of 750 claims and evidence articles.
   • Covers diverse health topics like nutrition, immune system, etc.

4. Baselines

   • Pipeline and joint systems tested with different base models.
   • Joint systems outperformed pipeline systems.

5. Experiments

   • Extensive experiments conducted on English version of the dataset.
   • Results show performance metrics for different models and tasks.

6. Challenges in Open-Domain Verification

   • Additional experiments conducted to test various scenarios like using Google snippets as evidence.

7. Discussion

   • Analysis of main results, qualitative error analysis, and challenges in open-domain verification discussed.

8. Conclusion

   • HealthFC dataset can advance automated fact-checking in healthcare.

Statisztikák

The dataset consists of 750 health-related claims labeled by medical experts.
Evidence sentences are manually annotated from clinical studies and systematic reviews.

Idézetek

"Fact-checking has emerged as an approach to assess the veracity of factual claims using evidence from credible knowledge sources."
"The dataset enables testing of various NLP tasks related to automated fact-checking."

Főbb Kivonatok

HealthFC

by Juraj Vladik... : arxiv.org 03-26-2024

https://arxiv.org/pdf/2309.08503.pdf

Mélyebb kérdések

How can the HealthFC dataset be utilized to improve automated fact-checking processes?

The HealthFC dataset can significantly enhance automated fact-checking processes by providing a rich source of health-related claims, evidence documents, and veracity labels. This dataset is unique in that it includes not only the claims but also detailed evidence from clinical trials and systematic reviews, labeled for veracity by medical experts. By utilizing this dataset, natural language processing (NLP) models can be trained to accurately assess the trustworthiness of health-related information on the internet. The dataset enables testing of various NLP tasks related to automated fact-checking such as evidence retrieval, claim verification, or explanation generation.

What are the implications of using domain-specific models like BioBERT for fact-checking in healthcare?

Using domain-specific models like BioBERT for fact-checking in healthcare has several implications. Firstly, these models are fine-tuned on biomedical text data which contains specialized terminology and nuances specific to the healthcare domain. This allows them to better understand and process complex medical information compared to general-purpose language models. Secondly, domain-specific models like BioBERT have been optimized for tasks relevant to healthcare such as natural language inference in scientific texts. This optimization leads to improved performance when verifying health claims based on clinical studies and research findings.

How can open-domain verification challenges be addressed using sources beyond Google snippets?

Open-domain verification challenges can be effectively addressed by leveraging diverse sources beyond Google snippets. One approach is to utilize reputable knowledge bases such as Wikipedia or PubMed which contain a wealth of reliable information on various topics including healthcare. By extracting relevant evidence from these sources, NLP models can make more informed decisions when verifying claims related to health or other domains. Additionally, incorporating human-written summaries or explanations from trusted sources into the verification process can provide valuable context and insights that may not be present in short text snippets retrieved from search engines.