Applying Large Language Models for Causal Structure Learning in Non Small Cell Lung Cancer

• URL: http://arxiv.org/abs/2311.07191v1
• Date: Mon, 13 Nov 2023 09:31:14 GMT
• Title: Applying Large Language Models for Causal Structure Learning in Non Small Cell Lung Cancer
• Authors: Narmada Naik, Ayush Khandelwal, Mohit Joshi, Madhusudan Atre, Hollis Wright, Kavya Kannan, Scott Hill, Giridhar Mamidipudi, Ganapati Srinivasa, Carlo Bifulco, Brian Piening, Kevin Matlock
• Abstract summary: Causal discovery is becoming a key part in medical AI research. In this paper, we investigate applying Large Language Models to the problem of determining the directionality of edges in causal discovery. Our result shows that LLMs can accurately predict the directionality of edges in causal graphs, outperforming existing state-of-the-art methods.
• Score: 8.248361703850774
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Causal discovery is becoming a key part in medical AI research. These methods can enhance healthcare by identifying causal links between biomarkers, demographics, treatments and outcomes. They can aid medical professionals in choosing more impactful treatments and strategies. In parallel, Large Language Models (LLMs) have shown great potential in identifying patterns and generating insights from text data. In this paper we investigate applying LLMs to the problem of determining the directionality of edges in causal discovery. Specifically, we test our approach on a deidentified set of Non Small Cell Lung Cancer(NSCLC) patients that have both electronic health record and genomic panel data. Graphs are validated using Bayesian Dirichlet estimators using tabular data. Our result shows that LLMs can accurately predict the directionality of edges in causal graphs, outperforming existing state-of-the-art methods. These findings suggests that LLMs can play a significant role in advancing causal discovery and help us better understand complex systems.

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