Applying Large Language Models for Causal Structure Learning in Non Small Cell Lung Cancer
\NameNarmada Naik1 1dātma Health Science, Beaverton, OR, USA, 2Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA \NameAyush Khandelwal1 1dātma Health Science, Beaverton, OR, USA, 2Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA \NameMohit Joshi1 1dātma Health Science, Beaverton, OR, USA, 2Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA \NameMadhusudan Atre1 1dātma Health Science, Beaverton, OR, USA, 2Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA \NameHollis Wright1 1dātma Health Science, Beaverton, OR, USA, 2Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA \NameKavya Kannan1 1dātma Health Science, Beaverton, OR, USA, 2Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA \NameScott Hill1 1dātma Health Science, Beaverton, OR, USA, 2Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA \NameGiridhar Mamidipudi1 1dātma Health Science, Beaverton, OR, USA, 2Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA \NameGanapati Srinivasa1 1dātma Health Science, Beaverton, OR, USA, 2Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA \NameCarlo Bifulco2 1dātma Health Science, Beaverton, OR, USA, 2Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA \NameBrian Piening2 1dātma Health Science, Beaverton, OR, USA, 2Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA \NameKevin Matlock1 1dātma Health Science, Beaverton, OR, USA, 2Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA
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.
原文 arXiv:2311.07191;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2311.07191v1