Enhancing stratification for survival analyses across standardized data sources.

backgroundPatient stratification is crucial for advancing personalized medicine yet is complicated due to the fragmented and variable nature of healthcare data. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) addresses the challenges regarding the data by offering a standardized framework for data integration across diverse sources and configurations. Recent advancements in machine learning, particularly transformer-based models such as Bidirectional Encoder Representations from Transformers (BERT), have demonstrated significant potential in extracting deep patient representations from electronic health records.

methodsThis study assesses the efficacy of BERT-based patient representation learning using OMOP CDM data for patient stratification. We harmonize originally incompatible datasets, including the established MIMIC-IV-2.2 and lung cancer data from the German cancer registry Schleswig-Holstein, within the OMOP CDM framework. BERT is pre-trained on the MIMIC-IV-2.2 dataset, and the derived representations are utilized to generate patient embeddings from the cancer registry (test) data. We employ k-means clustering on the embeddings to stratify patient subgroups. To evaluate whether the embeddings are useful for clustering, we divide the original cancer registry data into the corresponding groups and conduct survival analyses on selected columns for each cluster. The clustering method's effectiveness is assessed by comparing survival models trained on these clusters with naïve clusters derived only from the original dataset. In addition, we included a clinical expert review, in which a physician assessed the resulting cluster assignments for clinical plausibility and interpretability.

resultsOur approach effectively identifies patient similarities across datasets and allowed for efficient patient stratification. Survival analyses show varied performance depending on the model and cluster characteristics, with up to a 11% improvement over naïve k-means clusters, demonstrating the benefits of transfer learning. For some groups of patients, the corresponding accuracy of the survival analysis increased by up to 7%, emphasizing the value of stratifying homogeneous subgroups.

conclusionUtilizing standardized data and transformer-based foundation models to generate patient embeddings demonstrates effective knowledge transfer between two vastly different datasets and enables the identification of groups in which the accuracy of survival analysis can be significantly increased.