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V Rasmussen

Showing results (301-310 of 389) with videos related to

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Journal of Biomedical Informatics|July 10, 2016
Developing a data element repository to support EHR-driven phenotype algorithm authoring and executionGuoqian Jiang, Richard C Kiefer, Luke V Rasmussen, et al.
Learning Health Systems|October 21, 2020
Subphenotyping depression using machine learning and electronic health recordsZhenxing Xu, Fei Wang, Prakash Adekkanattu, et al.
European Heart Journal|March 8, 2014
Risk prediction of cardiovascular death based on the QTc interval: evaluating age and gender differences in a large primary care populationJonas B Nielsen, Claus Graff, Peter V Rasmussen, et al.
Learning Health Systems|October 21, 2020
Data-driven discovery of probable Alzheimer's disease and related dementia subphenotypes using electronic health recordsJie Xu, Fei Wang, Zhenxing Xu, et al.
Journal of Clinical and Translational Science|February 26, 2026
Going beyond the technology: Considerations in translating electronic case report formsAmy E Krefman, Luke V Rasmussen, Crystal Santillanes, et al.
Journal of the American Medical Informatics Association : JAMIA|July 31, 2015
Review and evaluation of electronic health records-driven phenotype algorithm authoring tools for clinical and translational researchJie Xu, Luke V Rasmussen, Pamela L Shaw, et al.
Applied Clinical Informatics|December 28, 2023
Seamless Integration of Computer-Adaptive Patient Reported Outcomes into an Electronic Health RecordKyle Nolla, Luke V Rasmussen, Nan E Rothrock, et al.
Journal of the American Medical Informatics Association : JAMIA|February 20, 2019
An ancillary genomics system to support the return of pharmacogenomic resultsLuke V Rasmussen, Maureen E Smith, Federico Almaraz, et al.
Applied Clinical Informatics|May 12, 2021
Infobuttons for Genomic Medicine: Requirements and BarriersLuke V Rasmussen, John J Connolly, Guilherme Del Fiol, et al.
Psychiatric Research and Clinical Practice|December 11, 2023
Using Machine Learning to Predict Antidepressant Treatment Outcome From Electronic Health RecordsZhenxing Xu, Veer Vekaria, Fei Wang, et al.
Pageof 39

Showing results (301-310 of 389) with videos related to

Sort By:
Pageof 39
Journal of Biomedical Informatics|July 10, 2016
Developing a data element repository to support EHR-driven phenotype algorithm authoring and executionGuoqian Jiang, Richard C Kiefer, Luke V Rasmussen, et al.
Learning Health Systems|October 21, 2020
Subphenotyping depression using machine learning and electronic health recordsZhenxing Xu, Fei Wang, Prakash Adekkanattu, et al.
European Heart Journal|March 8, 2014
Risk prediction of cardiovascular death based on the QTc interval: evaluating age and gender differences in a large primary care populationJonas B Nielsen, Claus Graff, Peter V Rasmussen, et al.
Learning Health Systems|October 21, 2020
Data-driven discovery of probable Alzheimer's disease and related dementia subphenotypes using electronic health recordsJie Xu, Fei Wang, Zhenxing Xu, et al.
Journal of Clinical and Translational Science|February 26, 2026
Going beyond the technology: Considerations in translating electronic case report formsAmy E Krefman, Luke V Rasmussen, Crystal Santillanes, et al.
Journal of the American Medical Informatics Association : JAMIA|July 31, 2015
Review and evaluation of electronic health records-driven phenotype algorithm authoring tools for clinical and translational researchJie Xu, Luke V Rasmussen, Pamela L Shaw, et al.
Applied Clinical Informatics|December 28, 2023
Seamless Integration of Computer-Adaptive Patient Reported Outcomes into an Electronic Health RecordKyle Nolla, Luke V Rasmussen, Nan E Rothrock, et al.
Journal of the American Medical Informatics Association : JAMIA|February 20, 2019
An ancillary genomics system to support the return of pharmacogenomic resultsLuke V Rasmussen, Maureen E Smith, Federico Almaraz, et al.
Applied Clinical Informatics|May 12, 2021
Infobuttons for Genomic Medicine: Requirements and BarriersLuke V Rasmussen, John J Connolly, Guilherme Del Fiol, et al.
Psychiatric Research and Clinical Practice|December 11, 2023
Using Machine Learning to Predict Antidepressant Treatment Outcome From Electronic Health RecordsZhenxing Xu, Veer Vekaria, Fei Wang, et al.
Pageof 39