KG-TREAT: Pre-training for Treatment Effect Estimation by Synergizing
  Patient Data with Knowledge Graphs
Ruoqi Liu Lingfei Wu Ping Zhang
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 a deep bi-level attention synergy method for
in-depth information fusion, enabling distinct encoding of treatment-covariate
and outcome-covariate relationships. KG-TREAT also incorporates two
pre-training tasks to ensure a thorough grounding and contextualization of
patient data and KGs. Evaluation on four downstream TEE tasks shows KG-TREAT's
superiority over existing methods, with an average improvement of 7% in Area
under the ROC Curve (AUC) and 9% in Influence Function-based Precision of
Estimating Heterogeneous Effects (IF-PEHE). The effectiveness of our estimated
treatment effects is further affirmed by alignment with established randomized
clinical trial findings.