Enabling scalable clinical interpretation of ML-based phenotypes using real world data

Contributions: (I) Conception and design: all authors; (II) Administrative support: AD, RD; (III) Provision of study materials or patients: RD; (IV) Collection and assembly of data: OP, NB; (V) Data analysis interpretation: OP, NB, JB, KP, PH, RD; (VI) Background : The availability of large and deep electronic healthcare records (EHR) datasets has the potential to enable a better understanding of real-world patient journeys, and to identify subgroups of patients currently grouped with a common disease label but differing in outcomes and remaining medical need. ML-based aggregation of EHR data is mostly tool-driven, i.e., building on available or newly developed methods. However, these methods, their input requirements, and, importantly, resulting output are frequently difficult to interpret, especially without in-depth data science or statistical training. This endangers the final step of analysis where an actionable and clinically meaningful interpretation is needed. Methods : We conducted a patient stratification and sub-phenotyping of cardiovascular patients from combination of NHS EHR datasets with encounters from March 2014 to August 2020 from Oxford University Hospital (OUH) and from February 2014 to March 2020 from Chelsea Hospital and Westminster Hospital (ChelWest). The dataset contained 1480 and 918 (aged 18 or older) from OUH and ChelWest respectively with EHR data containing diagnoses, laboratory tests, medications, and procedures. The use of different clustering methods and preprocessing steps resulted in more than 100 clinical reports which would require enormous amounts of time to be evaluated by clinical researchers. Results : We have developed several tools to facilitate the clinical evaluation and interpretation of unsupervised patient stratification results, namely pattern screening , meta clustering , surrogate modeling , and curation . These tools can be used at different stages within the analysis. As compared to a standard analysis approach, we demonstrate the ability to condense results and optimize analysis time. In the case of meta clustering , we demonstrate that the number of patient clusters can be reduced from 72 to 3 in one example. In another stratification result, by using surrogate models , we could quickly identify that heart failure patients were stratified if “blood sodium” measurements were available. As this is a routine measurement performed for all patients with heart failure, this indicated a data bias. By using further cohort and feature curation , these patients and other irrelevant features could be removed to increase the clinical meaningfulness. Conclusions : This study investigates approaches to perform patient stratification analysis at scale using large EHR datasets and multiple clustering methods for clinical research. We show examples on the effectiveness of the methods and hope to encourage further research in this field. between enrichment across clusters indicates the extent of over-enrichment (OR>1) or under-enrichment (OR<1) of a feature. With an increasing number of features in modern EHR datasets, direct enrichment analysis is impractical and alternative approaches, e.g., surrogate modelling, should be applied. A supervised decision tree finds criteria for cluster labels based on the important features in the model. Consequently, only the most important features are used which vastly simplifies the analysis.