%0 Journal Article
%T Stratifying heart failure patients with graph neural network and transformer using Electronic Health Records to optimize drug response prediction.
%A Chowdhury S
%A Chen Y
%A Li P
%A Rajaganapathy S
%A Wen A
%A Ma X
%A Dai Q
%A Yu Y
%A Fu S
%A Jiang X
%A He Z
%A Sohn S
%A Liu X
%A Bielinski SJ
%A Chamberlain AM
%A Cerhan JR
%A Zong N
%J J Am Med Inform Assoc
%V 0
%N 0
%D 2024 Jun 26
%M 38926131
%F 7.942
%R 10.1093/jamia/ocae137
%X <B>OBJECTIVE: </B>Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence, often fail to consider the unique characteristics of individual patients, resulting in suboptimal outcomes. This study aims to develop computational models that are patient-specific in predicting treatment outcomes, by utilizing a large Electronic Health Records (EHR) database. The goal is to improve drug response predictions by identifying specific HF patient subgroups that are likely to benefit from existing HF medications.<BR><B>METHODS: </B>A novel, graph-based model capable of predicting treatment responses, combining Graph Neural Network and Transformer was developed. This method differs from conventional approaches by transforming a patient's EHR data into a graph structure. By defining patient subgroups based on this representation via K-Means Clustering, we were able to enhance the performance of drug response predictions.<BR><B>RESULTS: </B>Leveraging EHR data from 11 627 Mayo Clinic HF patients, our model significantly outperformed traditional models in predicting drug response using NT-proBNP as a HF biomarker across five medication categories (best RMSE of 0.0043). Four distinct patient subgroups were identified with differential characteristics and outcomes, demonstrating superior predictive capabilities over existing HF subtypes (best mean RMSE of 0.0032).<BR><B>CONCLUSIONS: </B>These results highlight the power of graph-based modeling of EHR in improving HF treatment strategies. The stratification of patients sheds light on particular patient segments that could benefit more significantly from tailored response predictions.<BR><B>CONCLUSIONS: </B>Longitudinal EHR data have the potential to enhance personalized prognostic predictions through the application of graph-based AI techniques.