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Found 7 papers, 0 papers with code
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.
Similarity-based prediction of Ejection Fraction in Heart Failure Patients
Biomedical research is increasingly employing real world evidence (RWE) to foster discoveries of novel clinical phenotypes and to better characterize long term effect of medical treatments.
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.
Binary Input Layer: Training of CNN models with binary input data
For the efficient execution of deep convolutional neural networks (CNN) on edge devices, various approaches have been presented which reduce the bit width of the network parameters down to 1 bit.
