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Interpreting deep embeddings for disease progression clustering
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We propose a novel approach for interpreting deep embeddings in the context of patient clustering. We evaluate our approach on a dataset of participants with type 2 diabetes from the UK Biobank, and demonstrate clinically meaningful insights into disease progression patterns.
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Cited by 1 Pith paper
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Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model
A causal transformer that predicts each patient's future disease diagnoses repeatedly as their health record grows, producing a continuous risk trajectory over time.
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