Using electronic health records to develop and validate a machine-learning tool to predict type 2 diabetes outcomes: a study protocol
| Autor: | Pedro Pereira Rodrigues, Erik Mayer, Ara Darzi, Ana Luisa Neves, Tony Willis, Ben Glampson, Abdulrahim Mulla |
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| Přispěvatelé: | Imperial College Healthcare NHS Trust- BRC Funding, National Institute for Health Research, Imperial College Healthcare NHS Trust, NHS North West London CCG |
| Rok vydání: | 2021 |
| Předmět: |
diabetes & endocrinology
030209 endocrinology & metabolism Health Informatics Population health Disease Machine learning computer.software_genre Health informatics 1117 Public Health and Health Services health & safety Machine Learning 03 medical and health sciences 0302 clinical medicine Health care Medicine Electronic Health Records Humans 030212 general & internal medicine Retrospective Studies Receiver operating characteristic business.industry 1103 Clinical Sciences Retrospective cohort study Bayes Theorem General Medicine Integrated care Clinical trial Diabetes Mellitus Type 2 Artificial intelligence business computer 1199 Other Medical and Health Sciences |
| Zdroj: | BMJ Open BMJ Open, Vol 11, Iss 7 (2021) |
| ISSN: | 2044-6055 |
| Popis: | IntroductionType 2 diabetes mellitus (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 sociodemographic determinants, self-management ability or access to healthcare. Additionally, most tools are based in clinical trials, with limited external generalisability.ObjectiveThe 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 designRetrospective cohort study of patients with diagnosis of T2DM on 1 January 2015, with a 5-year follow-up. Anonymised electronic healthcare records from the Whole System Integrated Care (WSIC) database will be used.Preliminary outcomesOutcome 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 multidependence Bayesian networks. 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 curve in the derivation cohort with those calculated from a leave-one-out and a 10 times twofold cross-validation.Ethics and disseminationThe study has received approvals from the Information Governance Committee at the WSIC. Results will be made available to people with T2DM, their caregivers, the funders, diabetes care societies and other researchers. |
| Databáze: | OpenAIRE |
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