ArticleJournal of biomedical informatics2015
Implications of non-stationarity on predictive modeling using EHRs.
Article in Journal of biomedical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Graph Neural Networks in Neuroimaging: Current Status and Biostatistical Considerations for Clinical Deployment.Annals of biomedical engineering · 2026Review
- Transferring healthcare risk prediction models between Medicaid populations: a transfer learning evaluation.npj health systems · 2026Article
- Leveraging Ensemble Machine Learning Models for the Detection of Primary Myelofibrosis in Electronic Health Records.Cancers · 2026Article
- Real time machine learning prediction of next generation sequencing test results in live clinical settings.NPJ digital medicine · 2025Article
- Monitoring strategies for continuous evaluation of deployed clinical prediction models.Journal of biomedical informatics · 2025Article
- Spatiotemporal clusters of acute respiratory infections associated with socioeconomic, meteorological, and air pollution factors in South Punjab, Pakistan.BMC public health · 2025Article
- Pathophysiological Features in Electronic Medical Records Sustain Model Performance under Temporal Dataset Shift.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- DEPLOYR: a technical framework for deploying custom real-time machine learning models into the electronic medical record.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Machine learning versus traditional methods for the development of risk stratification scores: a case study using original Canadian Syncope Risk Score data.Internal and emergency medicine · 2022Article
- A Precision Medicine Agenda in Traumatic Brain Injury.Frontiers in pharmacology · 2022Review
- Personalized antibiograms for machine learning driven antibiotic selection.Communications medicine · 2022Article
- Development of a Model to Predict Healing of Chronic Wounds Within 12 Weeks.Advances in wound care · 2020Article
- Research and Reporting Considerations for Observational Studies Using Electronic Health Record Data.Annals of internal medicine · 2020Review
- Development and External Validation of a Deep Learning Algorithm for Prognostication of Cardiovascular Outcomes.Korean circulation journal · 2020Article
- Enhancing Prediction Models for One-Year Mortality in Patients with Acute Myocardial Infarction and Post Myocardial Infarction Syndrome.Studies in health technology and informatics · 2019Article
- Predicting the need for a reduced drug dose, at first prescription.Scientific reports · 2018Article
- High-fidelity phenotyping: richness and freedom from bias.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Scalable Electronic Phenotyping For Studying Patient Comorbidities.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- A dataset quantifying polypharmacy in the United States.Scientific data · 2017Article
- Electronic phenotyping with APHRODITE and the Observational Health Sciences and Informatics (OHDSI) data network.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2017Article
Corrections and comments
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2 authors.
Funding
Abstract
The rapidly increasing volume of clinical information captured in Electronic Health Records (EHRs) has led to the application of increasingly sophisticated models for purposes such as disease subtype discovery and predictive modeling. However, increasing adoption of EHRs implies that in the near future, much of the data available for such purposes will be from a time period during which both the practice of medicine and the clinical use of EHRs are in flux due to historic changes in both technology and incentives. In this work, we explore the implications of this phenomenon, called non-stationarity, on predictive modeling. We focus on the problem of predicting delayed wound healing using data available in the EHR during the first week of care in outpatient wound care centers, using a large dataset covering over 150,000 individual wounds and 59,958 patients seen over a period of four years. We manipulate the degree of non-stationarity seen by the model development process by changing the way data is split into training and test sets. We demonstrate that non-stationarity can lead to quite different conclusions regarding the relative merits of different models with respect to predictive power and calibration of their posterior probabilities. Under the non-stationarity exhibited in this dataset, the performance advantage of complex methods such as stacking relative to the best simple classifier disappears. Ignoring non-stationarity can thus lead to sub-optimal model selection in this task.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.