Nurses lack knowledge regarding natural language processing. The following presentation aims to narrow this practice gap through providing education on the process of natural language processing - a computerized technique to glean insights from large datasets.
Accreditation Statement: University of Texas at Austin School of Nursing is accredited as a provider of nursing continuing professional development by the American Nurses Credentialing Center's Commission on Accreditation.
Requirements for Successful Completion: To receive contact hours for this continuing education activity, the participant must complete the entire online module and complete and submit the evaluation form. Once the evaluation form has been submitted, a “Certificate of Successful Completion” will be awarded for 1.0 contact hours and will be available in your Learning Express account under "View/Print CE Credit".
Learning outcome: Registered nurses will report desire to change practice related to knowledge increase regarding empowering patients to use health technology to address chronic disease self-management. Focus will be on challenges specific to the aging population, youth, and underserved populations, exploring digital health research possibilities and limitations within the framework of ethical and legal boundaries.
The activity’s Nurse Planner has determined that no one who has the ability to control the content of this CNE activity – planning committee members and presenters/authors/content reviewers – has a conflict of interest.
This activity expires May 1, 2024
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Fee
$20.00
CE Hours
1.00
CE Units
0.100
Activity Type
- Knowledge
Target Audience(s)
- Registered Nurses
- Researchers
Nurses lack knowledge regarding natural language processing. The following presentation aims to narrow this practice gap through providing education on the process of natural language processing - a computerized technique to glean insights from large datasets.
Speaker(s)/Author(s)
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Max Topaz, PhD
Brief Bio : Max Topaz's, PhD, RN, research focuses on improving human health via cutting-edge technologies. His team develops artificial intelligence solutions that help health providers to provide best care for their patients. Specifically, Dr. Topaz is developing an open source natural language processing software called NimlbeMiner that clinicians and researchers can use to mine millions of patient records. In addition, his team is developing and implementing several clinical decision support tools. For example, they are currently testing a patient prioritization tool PREVENT that assists with identifying high risk patients during hospital to homecare transitions. |
Release Date:
May 1, 2022
Credit Expiration Date:
May 1, 2024
CE Hours
1.00
Fee
$20.00