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20172020
most citedMachine learning and AI research for Patient Benefit: 20 Critical Questions on Transparency, Replicability, Ethics and Effectiveness

17 citations · 21 across the 4 of their papers we have counts for

collaborators

5 papers

stat.ME2020

How to estimate the association between change in a risk factor and a health outcome?

Michail Katsoulis, Alvina G Lai, Dimitra-Kleio Kipourou +8

Estimating the effect of a change in a particular risk factor and a chronic disease requires information on the risk factor from two time points; the enrolment and the first follow…

cs.CY201817 cited

Machine learning and AI research for Patient Benefit: 20 Critical Questions on Transparency, Replicability, Ethics and Effectiveness

Sebastian Vollmer, Bilal A. Mateen, Gergo Bohner +15

Machine learning (ML), artificial intelligence (AI) and other modern statistical methods are providing new opportunities to operationalize previously untapped and rapidly growing s…

cs.CL2018

Application of Clinical Concept Embeddings for Heart Failure Prediction in UK EHR data

Spiros Denaxas, Pontus Stenetorp, Sebastian Riedel +3

Electronic health records (EHR) are increasingly being used for constructing disease risk prediction models. Feature engineering in EHR data however is challenging due to their hig…

cs.AI20173 cited

Evaluation of Semantic Web Technologies for Storing Computable Definitions of Electronic Health Records Phenotyping Algorithms

Vaclav Papez, Spiros Denaxas, Harry Hemingway

Electronic Health Records are electronic data generated during or as a byproduct of routine patient care. Structured, semi-structured and unstructured EHR offer researchers unprece…

cs.LG20171 cited

Evaluation of Machine Learning Methods to Predict Coronary Artery Disease Using Metabolomic Data

Henrietta Forssen, Riyaz S. Patel, Natalie Fitzpatrick +4

Metabolomic data can potentially enable accurate, non-invasive and low-cost prediction of coronary artery disease. Regression-based analytical approaches however might fail to full…