ArticleStatistics in medicine2017
A comparison of risk prediction methods using repeated observations: an application to electronic health records for hemodialysis.
Article in Statistics in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 5 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review.Journal of nephrology · 2023Pooled it
- Pooled it
- Informative presence and observation in routine health data: A review of methodology for clinical risk prediction.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- Pooled it
- Early warning scores for detecting deterioration in adult hospital patients: systematic review and critical appraisal of methodology.BMJ (Clinical research ed.) · 2020Pooled it
- Predicting acute kidney injury at hospital re-entry using high-dimensional electronic health record data.PloS one · 2018Trial
- The use of blood test trends in cancer detection: a scoping review.Diagnostic and prognostic research · 2026Review
- Prediction of recovery grade in Bell's palsy using machine learning models.Scientific reports · 2026Article
- On the Importance of a Clear Definition of Time Horizon for Time-to-Event Dynamic Predictions: A Systematic Review and a Concrete Illustration in Kidney Transplantation.Clinical epidemiology · 2026Article
- Risk markers for sudden unexpected death in epilepsy: an observational, prospective, multicentre cohort study.Lancet (London, England) · 2025Observational
- Designing an Implementable Clinical Prediction Model for Near-Term Mortality and Long-Term Survival in Patients on Maintenance Hemodialysis.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2024Article
- Young Infants Clinical Signs Study 8-sign Algorithm for Identification of Sick Infants Adapted for Routine Home Visits: A Systematic Review and Critical Appraisal of its Measurement Properties.Global pediatric health · 2024Review
- Improving cardiovascular risk prediction through machine learning modelling of irregularly repeated electronic health records.European heart journal. Digital health · 2024Article
- Dynamic predictions from longitudinal CD4 count measures and time to death of HIV/AIDS patients using a Bayesian joint model.Scientific African · 2023Article
- Multivariate longitudinal data for survival analysis of cardiovascular event prediction in young adults: insights from a comparative explainable study.BMC medical research methodology · 2023Observational
- Model for Predicting Complications of Hemodialysis Patients Using Data From the Internet of Medical Things and Electronic Medical Records.IEEE journal of translational engineering in health and medicine · 2023Article
- Incremental value of risk factor variability for cardiovascular risk prediction in individuals with type 2 diabetes: results from UK primary care electronic health records.International journal of epidemiology · 2022Article
- Translating Data Analytics Into Improved Spine Surgery Outcomes: A Roadmap for Biomedical Informatics Research in 2021.Global spine journal · 2022Article
- Developing Clinical Prediction Models Using Primary Care Electronic Health Record Data: The Impact of Data Preparation Choices on Model Performance.Frontiers in epidemiology · 2022Article
- Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods.Diagnostic and prognostic research · 2020Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
An increasingly important data source for the development of clinical risk prediction models is electronic health records (EHRs). One of their key advantages is that they contain data on many individuals collected over time. This allows one to incorporate more clinical information into a risk model. However, traditional methods for developing risk models are not well suited to these irregularly collected clinical covariates. In this paper, we compare a range of approaches for using longitudinal predictors in a clinical risk model. Using data from an EHR for patients undergoing hemodialysis, we incorporate five different clinical predictors into a risk model for patient mortality. We consider different approaches for treating the repeated measurements including use of summary statistics, machine learning methods, functional data analysis, and joint models. We follow up our empirical findings with a simulation study. Overall, our results suggest that simple approaches perform just as well, if not better, than more complex analytic approaches. These results have important implication for development of risk prediction models with EHRs. Copyright © 2017 John Wiley & Sons, Ltd.
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