Imperial College London
Alternate

Professor the Lord Darzi of Denham PC KBE FRS FMedSci HonFREng

Faculty of Medicine, Department of Surgery & Cancer

Co-Director of the IGHI, Professor of Surgery
 
 
 
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Contact

 

+44 (0)20 3312 1310a.darzi

 
 
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Location

 

Queen Elizabeth the Queen Mother Wing (QEQM)St Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Neves:2021:10.1136/bmjopen-2020-046716,
author = {Neves, AL and Pereira, Rodrigues P and Mulla, A and Glampson, B and Willis, T and Mayer, E},
doi = {10.1136/bmjopen-2020-046716},
journal = {BMJ Open},
pages = {1--5},
title = {Using electronic health records to develop and validate a machine learning tool to predict type 2 diabetes outcomes: a study protocol},
url = {http://dx.doi.org/10.1136/bmjopen-2020-046716},
volume = {11},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Introduction: Type 2 diabetes (T2DM) is a major cause of blindness, kidney failure, myocardial infarction, stroke and lower limb amputation. We are still unable, however, to accurately predict or identify which patients are at a higher risk of deterioration. Most risk stratification tools do not account for novel factors such as socio-demographic determinants, self-management ability, or access to healthcare. Additionally, most tools are based in clinical trials, with limited external generalisability.Objective: The aim of this work is to design and validate a machine learning-based tool to identify patients with T2DM at high risk of clinical deterioration, based on a comprehensive set of patient level characteristics retrieved from a population health linked dataset.Sample and design: Retrospective cohort study of patients with diagnosis of T2DM on Jan 1st, 2015, with a 5-year follow-up. Anonymised electronic health care records from the Whole System Integrated Care (WSIC) database will be used. Preliminary outcomes: Outcome variables of clinical deterioration will include retinopathy, chronic renal disease, myocardial infarction, stroke, peripheral arterial disease, or death. Predictor variables will include sociodemographic and geographic data, patients’ ability to self-manage disease, clinical and metabolic parameters and healthcare service usage. Prognostic models will be defined using multi-dependence Bayesian networks (BN). The derivation cohort, comprising 80% of the patients, will be used to define the prognostic models. Model parameters will be internally validated by comparing the area under the receiver operating characteristic (ROC) curve (AUC) in the derivation cohort with those calculated from a leave-one-out and a 10 times 2-fold cross-validation. Ethics and dissemination: The study has received approvals from the Information Governance Committee at the Whole Systems Integrated Care. Results will be made available to people with type 2 diabetes
AU  - Neves,AL
AU  - Pereira,Rodrigues P
AU  - Mulla,A
AU  - Glampson,B
AU  - Willis,T
AU  - Mayer,E
DO  - 10.1136/bmjopen-2020-046716
EP  - 5
PY  - 2021///
SN  - 2044-6055
SP  - 1
TI  - Using electronic health records to develop and validate a machine learning tool to predict type 2 diabetes outcomes: a study protocol
T2  - BMJ Open
UR  - http://dx.doi.org/10.1136/bmjopen-2020-046716
UR  - https://bmjopen.bmj.com/content/11/7/e046716
UR  - http://hdl.handle.net/10044/1/90785
VL  - 11
ER  -
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