Development of a dynamic type 2 diabetes risk prediction tool: a UK
Biobank study
- URL: http://arxiv.org/abs/2104.10108v1
- Date: Tue, 20 Apr 2021 16:37:26 GMT
- Title: Development of a dynamic type 2 diabetes risk prediction tool: a UK
Biobank study
- Authors: Nikola Dolezalova, Massimo Cairo, Alex Despotovic, Adam T.C. Booth,
Angus B. Reed, Davide Morelli, David Plans
- Abstract summary: We developed a predictive 10-year type 2 diabetes risk score using 301 features from the UK Biobank dataset.
A Cox proportional hazards model slightly overperformed a DeepSurv model trained using the same features.
This tool can be used for clinical screening of individuals at risk of developing type 2 diabetes and to foster patient empowerment.
- Score: 0.8620335948752806
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Diabetes affects over 400 million people and is among the leading causes of
morbidity worldwide. Identification of high-risk individuals can support early
diagnosis and prevention of disease development through lifestyle changes.
However, the majority of existing risk scores require information about
blood-based factors which are not obtainable outside of the clinic. Here, we
aimed to develop an accessible solution that could be deployed digitally and at
scale. We developed a predictive 10-year type 2 diabetes risk score using 301
features derived from 472,830 participants in the UK Biobank dataset while
excluding any features which are not easily obtainable by a smartphone. Using a
data-driven feature selection process, 19 features were included in the final
reduced model. A Cox proportional hazards model slightly overperformed a
DeepSurv model trained using the same features, achieving a concordance index
of 0.818 (95% CI: 0.812-0.823), compared to 0.811 (95% CI: 0.806-0.815). The
final model showed good calibration. This tool can be used for clinical
screening of individuals at risk of developing type 2 diabetes and to foster
patient empowerment by broadening their knowledge of the factors affecting
their personal risk.
Related papers
- From Glucose Patterns to Health Outcomes: A Generalizable Foundation Model for Continuous Glucose Monitor Data Analysis [50.80532910808962]
We present GluFormer, a generative foundation model on biomedical temporal data based on a transformer architecture.
GluFormer generalizes to 15 different external datasets, including 4936 individuals across 5 different geographical regions.
It can also predict onset of future health outcomes even 4 years in advance.
arXiv Detail & Related papers (2024-08-20T13:19:06Z) - Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals.
Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
arXiv Detail & Related papers (2024-04-26T16:39:50Z) - MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data
Augmentation [58.93221876843639]
This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion.
It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space.
It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
arXiv Detail & Related papers (2023-10-04T01:36:30Z) - Diagnosis Uncertain Models For Medical Risk Prediction [80.07192791931533]
We consider a patient risk model which has access to vital signs, lab values, and prior history but does not have access to a patient's diagnosis.
We show that such all-cause' risk models have good generalization across diagnoses but have a predictable failure mode.
We propose a fix for this problem by explicitly modeling the uncertainty in risk prediction coming from uncertainty in patient diagnoses.
arXiv Detail & Related papers (2023-06-29T23:36:04Z) - Using Machine Learning Techniques to Identify Key Risk Factors for
Diabetes and Undiagnosed Diabetes [0.0]
This paper reviews a wide selection of machine learning models built to predict the presence of diabetes and the presence of undiagnosed diabetes.
The most relevant variables of the best performing models are then compared.
Blood osmolality, family history, the prevalance of various compounds, and hypertension are key indicators for all diabetes risk.
arXiv Detail & Related papers (2021-05-19T20:02:35Z) - Development of digitally obtainable 10-year risk scores for depression
and anxiety in the general population [0.0]
We developed a 10-year predictive algorithm for depression and anxiety using the full cohort of over 400,000 UK Biobank participants.
If deployed in a digital solution, it would allow individuals to track their risk, as well as provide some pointers to how to decrease it through lifestyle changes.
arXiv Detail & Related papers (2021-04-20T16:16:56Z) - Development of an accessible 10-year Digital CArdioVAscular (DiCAVA)
risk assessment: a UK Biobank study [0.46180371154032895]
The aim was to develop a new risk model (DiCAVA) using statistical and machine learning techniques.
A secondary goal was to identify new patient-centric variables that could be incorporated into CVD risk assessments.
arXiv Detail & Related papers (2021-04-20T16:01:50Z) - Bootstrapping Your Own Positive Sample: Contrastive Learning With
Electronic Health Record Data [62.29031007761901]
This paper proposes a novel contrastive regularized clinical classification model.
We introduce two unique positive sampling strategies specifically tailored for EHR data.
Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
arXiv Detail & Related papers (2021-04-07T06:02:04Z) - Risk factor identification for incident heart failure using neural
network distillation and variable selection [24.366241122862473]
We propose two methods to untangle hidden patterns learned by an established deep learning model for risk association identification.
A cohort with 788,880 (8.3% incident heart failure) patients was considered for the study.
Model distillation identified 598 and 379 diseases that were associated and dissociated with heart failure at the population level, respectively.
In addition to these important population-level insights, we developed an approach to individual-level interpretation to take account of varying manifestation of heart failure in clinical practice.
arXiv Detail & Related papers (2021-02-17T10:20:38Z) - An explainable Transformer-based deep learning model for the prediction
of incident heart failure [22.513476932615845]
We developed a novel Transformer deep-learning model for prediction of incident heart failure involving 100,071 patients.
The model achieved 0.93 and 0.93 area under the receiver operator curve and 0.69 and 0.70 area under the precision-recall curve.
The importance of contextualised medical information was revealed in sensitivity analyses.
arXiv Detail & Related papers (2021-01-27T12:45:15Z) - UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced
Data [81.00385374948125]
We present UNcertaInTy-based hEalth risk prediction (UNITE) model.
UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data.
We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD)
UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
arXiv Detail & Related papers (2020-10-22T02:28:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.