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KG-TREAT: Pre-training for Treatment Effect Estimation by Synergizing Patient Data with Knowledge Graphs
March 7, 2024, 5:41 a.m. | Ruoqi Liu, Lingfei Wu, Ping Zhang
cs.LG updates on arXiv.org arxiv.org
Abstract: Treatment effect estimation (TEE) is the task of determining the impact of various treatments on patient outcomes. Current TEE methods fall short due to reliance on limited labeled data and challenges posed by sparse and high-dimensional observational patient data. To address the challenges, we introduce a novel pre-training and fine-tuning framework, KG-TREAT, which synergizes large-scale observational patient data with biomedical knowledge graphs (KGs) to enhance TEE. Unlike previous approaches, KG-TREAT constructs dual-focus KGs and integrates …
abstract arxiv challenges cs.ai cs.lg current data graphs impact knowledge knowledge graphs patient pre-training reliance training treatment type
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