Identifying and Aligning Medical Claims Made on Social Media with Medical Evidence

• URL: http://arxiv.org/abs/2405.11219v1
• Date: Sat, 18 May 2024 07:50:43 GMT
• Title: Identifying and Aligning Medical Claims Made on Social Media with Medical Evidence
• Authors: Anthony Hughes, Xingyi Song,
• Abstract summary: We study three core tasks: identifying medical claims, extracting medical vocabulary from these claims, and retrieving evidence relevant to those identified medical claims. We propose a novel system that can generate synthetic medical claims to aid each of these core tasks.
• Score: 0.12277343096128711
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evidence-based medicine is the practice of making medical decisions that adhere to the latest, and best known evidence at that time. Currently, the best evidence is often found in the form of documents, such as randomized control trials, meta-analyses and systematic reviews. This research focuses on aligning medical claims made on social media platforms with this medical evidence. By doing so, individuals without medical expertise can more effectively assess the veracity of such medical claims. We study three core tasks: identifying medical claims, extracting medical vocabulary from these claims, and retrieving evidence relevant to those identified medical claims. We propose a novel system that can generate synthetic medical claims to aid each of these core tasks. We additionally introduce a novel dataset produced by our synthetic generator that, when applied to these tasks, demonstrates not only a more flexible and holistic approach, but also an improvement in all comparable metrics. We make our dataset, the Expansive Medical Claim Corpus (EMCC), available at https://zenodo.org/records/8321460

Related papers

• MedKP: Medical Dialogue with Knowledge Enhancement and Clinical Pathway Encoding [48.348511646407026]
  We introduce the Medical dialogue with Knowledge enhancement and clinical Pathway encoding framework. The framework integrates an external knowledge enhancement module through a medical knowledge graph and an internal clinical pathway encoding via medical entities and physician actions.
  arXiv  Detail & Related papers  (2024-03-11T10:57:45Z)
• Comparing Knowledge Sources for Open-Domain Scientific Claim Verification [6.726255259929497]
  We show that PubMed works better with specialized biomedical claims, while Wikipedia is more suited for everyday health concerns. We discuss the results, outline frequent retrieval patterns and challenges, and provide promising future directions.
  arXiv  Detail & Related papers  (2024-02-05T09:57:15Z)
• Yes, this is what I was looking for! Towards Multi-modal Medical Consultation Concern Summary Generation [46.42604861624895]
  We propose a new task of multi-modal medical concern summary generation. Nonverbal cues, such as patients' gestures and facial expressions, aid in accurately identifying patients' concerns. We construct the first multi-modal medical concern summary generation corpus.
  arXiv  Detail & Related papers  (2024-01-10T12:56:47Z)
• Leveraging Generative AI for Clinical Evidence Summarization Needs to Ensure Trustworthiness [47.51360338851017]
  Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information. Recent advancements in generative AI, exemplified by large language models, hold promise in facilitating the arduous task.
  arXiv  Detail & Related papers  (2023-11-19T03:29:45Z)
• HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking [5.065947993017158]
  HealthFC is a dataset of 750 health-related claims in German and English labeled for veracity by medical experts. We provide an analysis of the dataset, highlighting its characteristics and challenges. We show that the dataset is a challenging test bed with a high potential for future use.
  arXiv  Detail & Related papers  (2023-09-15T16:05:48Z)
• Effective Medical Code Prediction via Label Internal Alignment [2.538209532048867]
  We propose a multi-view attention based Neural network to predict medical codes from clinical texts. Our method is verified to be effective on the open source dataset.
  arXiv  Detail & Related papers  (2023-05-09T04:14:20Z)
• Retrieval-Augmented and Knowledge-Grounded Language Models for Faithful Clinical Medicine [68.7814360102644]
  We propose the Re$3$Writer method with retrieval-augmented generation and knowledge-grounded reasoning. We demonstrate the effectiveness of our method in generating patient discharge instructions.
  arXiv  Detail & Related papers  (2022-10-23T16:34:39Z)
• RedHOT: A Corpus of Annotated Medical Questions, Experiences, and Claims on Social Media [1.5293427903448022]
  We present Reddit Health Online Talk (RedHOT), a corpus of 22,000 richly annotated social media posts from Reddit spanning 24 health conditions. We mark snippets that describe patient Populations, Interventions, and Outcomes (PIO elements) within these claims. We propose a new method to automatically derive (noisy) supervision for this task which we use to train a dense retrieval model.
  arXiv  Detail & Related papers  (2022-10-12T15:50:32Z)
• Conditional Generation Net for Medication Recommendation [73.09366442098339]
  Medication recommendation targets to provide a proper set of medicines according to patients' diagnoses, which is a critical task in clinics. We propose Conditional Generation Net (COGNet) which introduces a novel copy-or-predict mechanism to generate the set of medicines. We validate the proposed model on the public MIMIC data set, and the experimental results show that the proposed model can outperform state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-02-14T10:16:41Z)
• Towards more patient friendly clinical notes through language models and ontologies [57.51898902864543]
  We present a novel approach to automated medical text based on word simplification and language modelling. We use a new dataset pairs of publicly available medical sentences and a version of them simplified by clinicians. Our method based on a language model trained on medical forum data generates simpler sentences while preserving both grammar and the original meaning.
  arXiv  Detail & Related papers  (2021-12-23T16:11:19Z)
• Evidence Inference 2.0: More Data, Better Models [22.53884716373888]
  The Evidence Inference dataset was recently released to facilitate research toward this end. This paper collects additional annotations to expand the Evidence Inference dataset by 25%. The updated corpus, documentation, and code for new baselines and evaluations are available at http://evidence-inference.ebm-nlp.com/.
  arXiv  Detail & Related papers  (2020-05-08T17:16:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.