Development of the Centralized Interactive Phenomics Resource (CIPHER) Standard for Electronic Health Data-Based Phenomics Knowledgebase
Honerlaw, J.; Ho, Y.-L.; Fontin, F.; Gosian, J.; Maripuri, M.; Murray, M.; Sangar, R.; Galloway, A.; Zimolzak, A. J.; Whitbourne, S. B.; Casas, J. P.; Ramoni, R.; Gagnon, D. R.; Cai, T.; Liao, K. P.; Gaziano, J. M.; Muralidhar, S.; Cho, K.
Show abstract
The development of phenotypes using electronic health records is a resource intensive process. Therefore, the cataloging of phenotype algorithm metadata for reuse is critical to accelerate clinical research. The Department of Veterans Affairs Office of Research and Development has developed a phenomics knowledgebase library, CIPHER (Centralized Interactive Phenomics Research), which improves upon existing phenomics library models to help advance innovation in clinical research by using the CIPHER phenotype collection standard. The CIPHER standard was iteratively developed with phenomics experts and has been used to capture over 5,000 phenotypes. We describe the development of the CIPHER standard for phenotype metadata collection, its current application to the largest healthcare system in the United States, and the future expansion of the CIPHER knowledgebase as a public resource for phenotyping.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Development and Validation of Phenotype Classifiers across Multiple Sites in the Observational Health Sciences and Informatics (OHDSI) Network 95%
- Large Language Models Facilitate the Generation of Electronic Health Record Phenotyping Algorithms 93%
- Increasing Trust in Real-World Evidence Through Evaluation of Observational Data Quality 93%
Similar papers in this journal
- Development and Validation of ‘Patient Optimizer’ (POP) Algorithms for Predicting Surgical Risk with Machine Learning 91%
- Implicit bias in Critical Care Data: Factors affecting sampling frequencies and missingness patterns of clinical and biological variables in ICU Patients 91%
- Optimized Feature Selection and Advanced Machine Learning for Stroke Risk Prediction in Revascularized Coronary Artery Disease Patients 90%
Similar papers in this journal
- Clinical code sets and the problem of redundancy in code set repositories 95%
- CohortDiagnostics: phenotype evaluation across a network of observational data sources using population-level characterization 93%
- Association of Mortality and Aspirin Prescription for COVID-19 Patients at the Veterans Health Administration 93%
Similar papers in this journal
- LinkR: an open source, low-code and collaborative data science platform for healthcare data analysis and visualization 94%
- Machine Learning Directed Interventions Associate with Decreased Hospitalization Rates in Hemodialysis Patients 92%
- Predicting Prognosis in COVID-19 Patients using Machine Learning and Readily Available Clinical Data 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.