ArticleJournal of the American Medical Informatics Association : JAMIA2015
Parameterizing time in electronic health record studies.
Article in Journal of the American Medical Informatics Association : JAMIA, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03911687 (Communicating Narrative Concerns Entered by RNs), which is not on this map. Cited by 29 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Communicating Narrative Concerns Entered by RNs (CONCERN) Clinical Decision Support (CDS) System
Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Enhancing Patient Outcome Prediction Through Deep Learning With Sequential Diagnosis Codes From Structured Electronic Health Record Data: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Progress in Biomedical Knowledge Discovery: A 25-year Retrospective.Yearbook of medical informatics · 2016Pooled it
- A multiobjective optimization approach to data assimilation for complex biological systems with sparse data.Mathematical biosciences · 2026Article
- End-to-end extraction of temporal information from psychiatric discharge summaries.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Missing data matter: an empirical evaluation of the impacts of missing EHR data in comparative effectiveness research.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?Journal of biomedical informatics · 2023Article
- Impact of preoperative laboratory frailty index on mortality and clinical outcomes in older surgical patients with cancer.Scientific reports · 2022Article
- Evaluating Prediction of Continuous Clinical Values: A Glucose Case Study.Methods of information in medicine · 2022Article
- Timing errors and temporal uncertainty in clinical databases-A narrative review.Frontiers in digital health · 2022Review
- Developing and evaluating a pediatric asthma severity computable phenotype derived from electronic health records.The Journal of allergy and clinical immunology · 2021Article
- Estimating the efficacy of symptom-based screening for COVID-19.NPJ digital medicine · 2020Article
- Temporal biomedical data analytics.Journal of biomedical informatics · 2019Article
- Longitudinal analysis of social and behavioral determinants of health in the EHR: exploring the impact of patient trajectories and documentation practices.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- Methodological variations in lagged regression for detecting physiologic drug effects in EHR data.Journal of biomedical informatics · 2018Article
- Modeling asynchronous event sequences with RNNs.Journal of biomedical informatics · 2018Article
- Biases in electronic health record data due to processes within the healthcare system: retrospective observational study.BMJ (Clinical research ed.) · 2018Observational
- High-fidelity phenotyping: richness and freedom from bias.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Estimating summary statistics for electronic health record laboratory data for use in high-throughput phenotyping algorithms.Journal of biomedical informatics · 2018Article
- The Association Between Ventilator Dyssynchrony, Delivered Tidal Volume, and Sedation Using a Novel Automated Ventilator Dyssynchrony Detection Algorithm.Critical care medicine · 2018Observational
- Yield and bias in defining a cohort study baseline from electronic health record data.Journal of biomedical informatics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundFields like nonlinear physics offer methods for analyzing time series, but many methods require that the time series be stationary-no change in properties over time.Objective Medicine is far from stationary, but the challenge may be able to be ameliorated by reparameterizing time because clinicians tend to measure patients more frequently when they are ill and are more likely to vary.
methodsWe compared time parameterizations, measuring variability of rate of change and magnitude of change, and looking for homogeneity of bins of temporal separation between pairs of time points. We studied four common laboratory tests drawn from 25 years of electronic health records on 4 million patients.
resultsWe found that sequence time-that is, simply counting the number of measurements from some start-produced more stationary time series, better explained the variation in values, and had more homogeneous bins than either traditional clock time or a recently proposed intermediate parameterization. Sequence time produced more accurate predictions in a single Gaussian process model experiment.
conclusionsOf the three parameterizations, sequence time appeared to produce the most stationary series, possibly because clinicians adjust their sampling to the acuity of the patient. Parameterizing by sequence time may be applicable to association and clustering experiments on electronic health record data. A limitation of this study is that laboratory data were derived from only one institution. Sequence time appears to be an important potential parameterization.
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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.