ArticleJournal of the American Medical Informatics Association : JAMIA2011
Exploiting time in electronic health record correlations.
Article in Journal of the American Medical Informatics Association : JAMIA, 2011. 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 45 papers, 3 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
45 citing papers in PubMed, 3 syntheses or guidelines pooled it, 70 citations in OpenAlex.
- Development and validation of early warning score system: A systematic literature review.Journal of biomedical informatics · 2020Pooled it
- Progress in Biomedical Knowledge Discovery: A 25-year Retrospective.Yearbook of medical informatics · 2016Pooled it
- Innovative information visualization of electronic health record data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2015Pooled it
- A multiobjective optimization approach to data assimilation for complex biological systems with sparse data.Mathematical biosciences · 2026Article
- Can Real-World Data From Hospital Information Systems Be Directly Utilized for Efficacy Studies: An Analysis of Data Time Points.Journal of evidence-based medicine · 2025Article
- Developing A Data Pipeline to Quantify Ventilator Waveforms.medRxiv : the preprint server for health sciences · 2025Article
- LabVis: usability testing of a prototype tool for integrating timeline graphs and clinical notes.BMC medical informatics and decision making · 2025Article
- Article
- A simple modeling framework for prediction in the human glucose-insulin system.Chaos (Woodbury, N.Y.) · 2023Article
- 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
- High-throughput phenotyping with temporal sequences.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Generative transfer learning for measuring plausibility of EHR diagnosis records.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Temporal relationship of computed and structured diagnoses in electronic health record data.BMC medical informatics and decision making · 2021Article
- Phenotype risk scores (PheRS) for pancreatic cancer using time-stamped electronic health record data: Discovery and validation in two large biobanks.Journal of biomedical informatics · 2021Article
- Transitive Sequencing Medical Records for Mining Predictive and Interpretable Temporal Representations.Patterns (New York, N.Y.) · 2020Article
- The parameter Houlihan: A solution to high-throughput identifiability indeterminacy for brutally ill-posed problems.Mathematical biosciences · 2019Article
- Comorbidity Characterization Among eMERGE Institutions: A Pilot Evaluation with the Johns Hopkins Adjusted Clinical Groups® System.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Leveraging Clinical Expertise as a Feature - not an Outcome - of Predictive Models: Evaluation of an Early Warning System Use Case.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- A roadmap for semi-automatically extracting predictive and clinically meaningful temporal features from medical data for predictive modeling.Global transitions · 2019Article
- An evaluation of clinical order patterns machine-learned from clinician cohorts stratified by patient mortality outcomes.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 at 1 institution in 1 country.
Funding
Abstract
objectiveTo demonstrate that a large, heterogeneous clinical database can reveal fine temporal patterns in clinical associations; to illustrate several types of associations; and to ascertain the value of exploiting time. MATERIALS AND
methodsLagged linear correlation was calculated between seven clinical laboratory values and 30 clinical concepts extracted from resident signout notes from a 22-year, 3-million-patient database of electronic health records. Time points were interpolated, and patients were normalized to reduce inter-patient effects.
resultsThe method revealed several types of associations with detailed temporal patterns. Definitional associations included low blood potassium preceding 'hypokalemia.' Low potassium preceding the drug spironolactone with high potassium following spironolactone exemplified intentional and physiologic associations, respectively. Counterintuitive results such as the fact that diseases appeared to follow their effects may be due to the workflow of healthcare, in which clinical findings precede the clinician's diagnosis of a disease even though the disease actually preceded the findings. Fully exploiting time by interpolating time points produced less noisy results. DISCUSSION: Electronic health records are not direct reflections of the patient state, but rather reflections of the healthcare process and the recording process. With proper techniques and understanding, and with proper incorporation of time, interpretable associations can be derived from a large clinical database.
conclusionA large, heterogeneous clinical database can reveal clinical associations, time is an important feature, and care must be taken to interpret the results.
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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.