ArticleJournal of the American Medical Informatics Association : JAMIA2013
Temporal phenome analysis of a large electronic health record cohort enables identification of hospital-acquired complications.
Article in Journal of the American Medical Informatics Association : JAMIA, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Progress in Biomedical Knowledge Discovery: A 25-year Retrospective.Yearbook of medical informatics · 2016Pooled it
- Identifying nursing sensitive indicators from electronic health records in acute cardiac care-Towards intelligent automated assessment of care quality.Journal of nursing management · 2022Article
- Domain Adaptation Using Convolutional Autoencoder and Gradient Boosting for Adverse Events Prediction in the Intensive Care Unit.Frontiers in artificial intelligence · 2022Article
- Sleep health, diseases, and pain syndromes: findings from an electronic health record biobank.Sleep · 2021Article
- Digital health technologies: opportunities and challenges in rheumatology.Nature reviews. Rheumatology · 2020Review
- An Analysis of the Learning Health System in Its First Decade in Practice: Scoping Review.Journal of medical Internet research · 2020Article
- High-fidelity phenotyping: richness and freedom from bias.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Clinical Informatics Researcher's Desiderata for the Data Content of the Next Generation Electronic Health Record.Applied clinical informatics · 2017Article
- TEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records.Scientific reports · 2017Article
- Combination of static and temporal data analysis to predict mortality and readmission in the intensive care.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2017Article
- Phenome-Wide Association Studies as a Tool to Advance Precision Medicine.Annual review of genomics and human genetics · 2016Review
- Classification of hospital acquired complications using temporal clinical information from a large electronic health record.Journal of biomedical informatics · 2016Article
- Parameterizing time in electronic health record studies.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Seeing the forest through the trees: uncovering phenomic complexity through interactive network visualization.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- "Big data" and the electronic health record.Yearbook of medical informatics · 2014Review
- Review
- EHR-based phenome wide association study in pancreatic cancer.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2014Article
- Electronic health records-driven phenotyping: challenges, recent advances, and perspectives.Journal of the American Medical Informatics Association : JAMIA · 2013Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveTo develop methods for visual analysis of temporal phenotype data available through electronic health records (EHR). MATERIALS AND
methods24 580 adults from the multiparameter intelligent monitoring in intensive care V.6 (MIMIC II) EHR database of critically ill patients were analyzed, with significant temporal associations visualized as a map of associations between hospital length of stay (LOS) and ICD-9-CM codes. An expanded phenotype, using ICD-9-CM, microbiology, and computerized physician order entry data, was defined for hospital-acquired Clostridium difficile (HA-CDI). LOS, estimated costs, 30-day post-discharge mortality, and antecedent medication provider order entry were evaluated for HA-CDI cases compared to randomly selected controls.
resultsTemporal phenome analysis revealed 191 significant codes (p value, adjusted for false discovery rate, ≤0.05). HA-CDI was identified in 414 cases, and was associated with longer median LOS, 20 versus 9 days, and adjusted HR 0.33 (95% CI 0.28 to 0.39). This prolongation carries an estimated annual incremental cost increase of US$1.2-2.0 billion in the USA alone. DISCUSSION: Comprehensive EHR data have made large-scale phenome-based analysis feasible. Time-dependent pathological disease states have dynamic phenomic evolution, which may be captured through visual analytical approaches. Although MIMIC II is a single institutional retrospective database, our approach should be portable to other EHR data sources, including prospective 'learning healthcare systems'. For example, interventions to prevent HA-CDI could be dynamically evaluated using the same techniques.
conclusionsThe new visual analytical method described in this paper led directly to the identification of numerous hospital-acquired conditions, which could be further explored through an expanded phenotype definition.
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