mlsanityData to 7 Oct 2026

Causality extraction from medical text using Large Language Models (LLMs)

arXiv 2407.10020 ↗First posted 13 Jul 2024cs.CL cs.AI cs.IRcited by 7

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Abstract (arXiv)

This study explores the potential of natural language models, including large language models, to extract causal relations from medical texts, specifically from Clinical Practice Guidelines (CPGs). The outcomes causality extraction from Clinical Practice Guidelines for gestational diabetes are presented, marking a first in the field. We report on a set of experiments using variants of BERT (BioBERT, DistilBERT, and BERT) and using Large Language Models (LLMs), namely GPT-4 and LLAMA2. Our experiments show that BioBERT performed better than other models, including the Large Language Models, with an average F1-score of 0.72. GPT-4 and LLAMA2 results show similar performance but less consistency. We also release the code and an annotated a corpus of causal statements within the Clinical Practice Guidelines for gestational diabetes.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 19 of its 73 references that are in the map. First posted on arXiv 13 Jul 2024.

Builds on

19 of 73 references.

References · 19

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  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
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7 more
  1. 2022e-CARE: a New Dataset for Exploring Explainable Causal Reasoningcited by 47
  2. 2023ChatGPT Evaluation on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relationscited by 39
  3. 2019Causality Extraction based on Self-Attentive BiLSTM-CRF with Transferred Embeddingscited by 25
  4. 2020Causal BERT : Language models for causality detection between events expressed in textcited by 22
  5. 2021HeadlineCause: A Dataset of News Headlines for Detecting Causalitiescited by 5
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  7. 2020Computing Conceptual Distances between Breast Cancer Screening Guidelines: An Implementation of a Near-Peer Epistemic Model of Medical Disagreementcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 1.

7 papers.

  1. 2025A Survey on Enhancing Causal Reasoning Ability of Large Language Modelscited by 8
  2. 2024Counterfactual Causal Inference in Natural Language with Large Language Modelscited by 7
  3. 2024A Survey of Event Causality Identification: Taxonomy, Challenges, Assessment, and Prospectscited by 6
  4. 2025Refine Medical Diagnosis Using Generation Augmented Retrieval and Clinical Practice Guidelinescited by 1
  5. 2026Benchmarking LLMs for Pairwise Causal Discovery in Biomedical and Multi-Domain Contextscited by 1
  6. 2025Quantifying Symptom Causality in Clinical Decision Making: An Exploration Using CausaLMcited by 0
  7. 2025Zero-Shot Event Causality Identification via Multi-source Evidence Fuzzy Aggregation with Large Language Modelscited by 0