RESEARCH
Creating dynamic visualizations to investigate research questions and to foster student learning outcomes.
DSC
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Research
Below are select projects from the DSC's research and work. If you are looking for a particular project's information and don't see it, please feel free to contact us via the Contact Page and we will be in touch!
Undiagnosed Patient EHR Note Inquiry
Department: CCHMC Hospital Medicine
Researcher: Trisha Marshall, MD; Phil Hagedorn, MD ; Pat Brady, MD
Dataset and Source: Language from physician notes (retrieved from Epic) for CCHMC Hospital Medicine patients who receive an undiagnosed (UD) designation.
Status: In Progress
DSC Team Members: Lindsay Nickels, Erin McCabe, Ezra Edgerton, Anubhav Maity, Sally Luken
Goals and/or Outcomes: Identify “uncertain” language in Hospital Medicine physician notes that may be used for predictions, improved patient care, and updated physician training. Eventually, these findings may be valuable to EHR software firms. Methods and tools include WordSmith Tools corpus linguistic software, and semi-supervised data analysis throughout the DSC platform and other resources.
Researcher: Trisha Marshall, MD; Phil Hagedorn, MD ; Pat Brady, MD
Dataset and Source: Language from physician notes (retrieved from Epic) for CCHMC Hospital Medicine patients who receive an undiagnosed (UD) designation.
Status: In Progress
DSC Team Members: Lindsay Nickels, Erin McCabe, Ezra Edgerton, Anubhav Maity, Sally Luken
Goals and/or Outcomes: Identify “uncertain” language in Hospital Medicine physician notes that may be used for predictions, improved patient care, and updated physician training. Eventually, these findings may be valuable to EHR software firms. Methods and tools include WordSmith Tools corpus linguistic software, and semi-supervised data analysis throughout the DSC platform and other resources.
Tags :
- Electronic Health Record Machine Learning Classification