Research graph
References from A Calibrated Glycaemic-Blind Machine-Learning Model for Opportunistic Prediabetes Screening in UK Biobank. Local targets link to admitted publications; unresolved targets remain external evidence.
Prediabetes: a high-risk state for diabetes development
10.1016/s0140-6736(12)60283-9 · 2012 · External reference
10.1016/j.jacc.2011.08.080
10.1016/j.jacc.2011.08.080 · 2012 · External reference
2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes-2025
10.2337/dc25-s002 · 2025 · External reference
Screening for Diabetes and Prediabetes
10.1016/j.ecl.2021.05.002 · 2021 · External reference
Algorithms for the capture and adjudication of prevalent and incident diabetes in UK Biobank
10.1371/journal.pone.0162388 · 2016 · External reference
Review of methods for detecting glycemic disorders
10.1016/j.diabres.2020.108233 · 2020 · External reference
Diagnostic accuracy of tests for type 2 diabetes and prediabetes: A systematic review and meta-analysis
10.1371/journal.pone.0242415 · 2020 · External reference
Assessment of the effect of the COVID-19 pandemic on UK HbA1c testing: implications for diabetes management and diagnosis
10.1136/jclinpath-2021-207776 · 2023 · External reference
Leakage and the reproducibility crisis in machine-learning-based science
10.1016/j.patter.2023.100804 · 2023 · External reference
Target leakage and the use of diagnostic variables in diabetes prediction models
10.1038/s41387-025-00396-5 · 2025 · External reference
LightGBM: A highly efficient gradient boosting decision tree
2017 · External reference
Calibration: the Achilles heel of predictive analytics
10.1186/s12916-019-1466-7 · 2019 · External reference
Minimum sample size for external validation of a clinical prediction model with a binary outcome
10.1002/sim.9025 · 2021 · External reference
Identifying top ten predictors of type 2 diabetes through machine learning analysis of UK Biobank data
10.1038/s41598-024-52023-5 · 2024 · External reference
Prediction model for type 2 diabetes mellitus and its association with mortality using machine learning in three independent cohorts from South Korea, Japan, and the UK: a model development and validation study
10.1016/j.eclinm.2025.103069 · 2025 · External reference
TRIPOD + AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods
10.1136/bmj-2023-078378 · 2024 · External reference
Assessing calibration and bias of a deployed machine learning malnutrition prediction model within a large healthcare system
10.1038/s41746-024-01141-5 · 2024 · External reference
A simple, step-by-step guide to interpreting decision curve analysis
10.1186/s41512-019-0064-7 · 2019 · External reference
The Diabetes Risk Score: A practical tool to predict type 2 diabetes risk
10.2337/diacare.26.3.725 · 2003 · External reference
Development and Validation of a Patient Self-assessment Score for Diabetes Risk
10.7326/0003-4819-151-11-200912010-00005 · 2009 · External reference
Unresolved reference
2017 · External reference
From local explanations to global understanding with explainable AI for trees
10.1038/s42256-019-0138-9 · 2020 · External reference
Finnish diabetes risk score (findrisc) for type 2 diabetes screening compared with the oral glucose tolerance test: A systematic review and meta-analysis of diagnostic test accuracy
10.1016/j.diabres.2025.112480 · 2025 · External reference
