Enabling scalable clinical interpretation of ML-based phenotypes using real world data. (arXiv:2208.01607v1 [cs.LG])

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 novel subgroups of patients. 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 …

arxiv data enabling interpretation lg ml real world data scalable