ArticlePloS one2014
Dynamical phenotyping: using temporal analysis of clinically collected physiologic data to stratify populations.
Article in PloS one, 2014. 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 27 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
27 citing papers in PubMed.
- Early ICU glycemic trajectory phenotypes predict short- and long-term mortality in critically ill patients: a dual-database cohort study.Metabolism open · 2026Article
- AI-Aristotle: A physics-informed framework for systems biology gray-box identification.PLoS computational biology · 2024Article
- A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation.Journal of biomedical informatics · 2023Article
- Who needs what (features) when? Personalizing engagement with data-driven self-management to improve health equity.Journal of biomedical informatics · 2023Article
- A simple modeling framework for prediction in the human glucose-insulin system.Chaos (Woodbury, N.Y.) · 2023Article
- Systems Biology: Identifiability Analysis and Parameter Identification via Systems-Biology-Informed Neural Networks.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Enabling personalized decision support with patient-generated data and attributable components.Journal of biomedical informatics · 2021Article
- Trajectories, bifurcations, and pseudo-time in large clinical datasets: applications to myocardial infarction and diabetes data.GigaScience · 2020Article
- A new approach to integrating patient-generated data with expert knowledge for personalized goal setting: A pilot study.International journal of medical informatics · 2020Article
- The parameter Houlihan: A solution to high-throughput identifiability indeterminacy for brutally ill-posed problems.Mathematical biosciences · 2019Article
- Preparing next-generation scientists for biomedical big data: artificial intelligence approaches.Personalized medicine · 2019Review
- Temporal biomedical data analytics.Journal of biomedical informatics · 2019Article
- Methodological variations in lagged regression for detecting physiologic drug effects in EHR data.Journal of biomedical informatics · 2018Article
- Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Biases in electronic health record data due to processes within the healthcare system: retrospective observational study.BMJ (Clinical research ed.) · 2018Observational
- A bibliometric analysis of natural language processing in medical research.BMC medical informatics and decision making · 2018Article
- 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
5 authors.
Funding
Abstract
Using glucose time series data from a well measured population drawn from an electronic health record (EHR) repository, the variation in predictability of glucose values quantified by the time-delayed mutual information (TDMI) was explained using a mechanistic endocrine model and manual and automated review of written patient records. The results suggest that predictability of glucose varies with health state where the relationship (e.g., linear or inverse) depends on the source of the acuity. It was found that on a fine scale in parameter variation, the less insulin required to process glucose, a condition that correlates with good health, the more predictable glucose values were. Nevertheless, the most powerful effect on predictability in the EHR subpopulation was the presence or absence of variation in health state, specifically, in- and out-of-control glucose versus in-control glucose. Both of these results are clinically and scientifically relevant because the magnitude of glucose is the most commonly used indicator of health as opposed to glucose dynamics, thus providing for a connection between a mechanistic endocrine model and direct insight to human health via clinically collected data.
Indexed as
Identifiers
What OpenQuestion holds
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.