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Found 4 papers, 1 papers with code
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.
Prediction of the onset of cardiovascular diseases from electronic health records using multi-task gated recurrent units
The proposed approach is compared to a standard clinical risk predictor (QRISK) and machine learning alternatives using 5-year data from a NHS Foundation Trust.
Semantic filtering through deep source separation on microscopy images
By their very nature microscopy images of cells and tissues consist of a limited number of object types or components.
