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Found 6 papers, 2 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.
Deep Semi-Supervised Embedded Clustering (DSEC) for Stratification of Heart Failure Patients
In this work we apply deep semi-supervised embedded clustering to determine data-driven patient subgroups of heart failure from the electronic health records of 4, 487 heart failure and control patients.
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.
Monitoring Depression in Bipolar Disorder using Circadian Measures from Smartphone Accelerometers
The objective of this study was to use acceleration data recorded from smartphones to predict levels of depression in a population of participants diagnosed with bipolar disorder.
Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECG
Similarly, the convolutional neural network scored 72. 1% on the augmented database and 83% on the test set.
