46 citations · 46 across the 4 of their papers we have counts for
4 papers
Enabling scalable clinical interpretation of ML-based phenotypes using real world data
Owen Parsons, Nathan E Barlow, Janie Baxter +4
The availability of large and deep electronic healthcare records (EHR) datasets has the potential to enable a better understanding of real-world patient journeys, and to identify n…
Compensating trajectory bias for unsupervised patient stratification using adversarial recurrent neural networks
Avelino Javer, Owen Parsons, Oliver Carr +6
Electronic healthcare records are an important source of information which can be used in patient stratification to discover novel disease phenotypes. However, they can be challeng…
Deep Semi-Supervised Embedded Clustering (DSEC) for Stratification of Heart Failure Patients
Oliver Carr, Stojan Jovanovic, Luca Albergante +6
Determining phenotypes of diseases can have considerable benefits for in-hospital patient care and to drug development. The structure of high dimensional data sets such as electron…
A comparative study of artificial intelligence and human doctors for the purpose of triage and diagnosis
Salman Razzaki, Adam Baker, Yura Perov +10
Online symptom checkers have significant potential to improve patient care, however their reliability and accuracy remain variable. We hypothesised that an artificial intelligence…