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Enabling scalable clinical interpretation of ML-based phenotypes using real world data
This endangers the final step of analysis where an actionable and clinically meaningful interpretation is needed. This study investigates approaches to perform patient stratification analysis at scale using large EHR datasets and multiple clustering methods for clinical research.
Compensating trajectory bias for unsupervised patient stratification using adversarial recurrent neural networks
Electronic healthcare records are an important source of information which can be used in patient stratification to discover novel disease phenotypes.
Longitudinal patient stratification of electronic health records with flexible adjustment for clinical outcomes
We demonstrate the model performance on $29, 229$ diabetes patients, showing it finds clusters of patients with both different trajectories and different outcomes which can be utilized to aid clinical decision making.
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