Using machine learning to improve the accuracy of patient deterioration predictions: Mayo Clinic Early Warning Score (MC-EWS)
Lead author Santiago Romero-Brufau, MD, PhD discusses this month’s JAMIA Journal Club selection:
Romero-Brufau S, Whitford D, Johnson MG, et al. Using machine learning to improve the accuracy of patient deterioration predictions: Mayo Clinic Early Warning Score (MC-EWS) [published online ahead of print, 2021 Feb 26]. J Am Med Inform Assoc. 2021;ocaa347. doi:10.1093/jamia/ocaa347 [Abstract]
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Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA
Department of Medicine, Mayo Clinic, Rochester, MN
Santiago Romero-Brufau, MD, PhD, is an Assistant Professor of Medicine and Healthcare Systems Engineering at Mayo Clinic, where he also serves as Principal Data Scientist for the Department of Medicine. His work focuses on the development and implementation of machine-learning models into clinical practice. He is also instructor in the Department of Biostatistics and in Health Data Science at the Harvard T.H. Chan School of Public Health.
- 35-minute presentation by article author(s) considering salient features of the published study and its potential impact on practice.
- 25-minute discussion of questions submitted by listeners via the webinar tools and moderated by JAMIA Student Editorial Board members
JAMIA Journal Club managers and monthly moderators are JAMIA Student Editorial Board members:
Statement of Purpose
Physiological deterioration in the hospital is often unrecognized and can lead to increased patient mortality. To improve the identification and response to acute deterioration, more than 70 early warning scores have been developed. Some recent models have used machine learning to increase prediction accuracy for adult acute inpatient deterioration, but the value of nursing assessments and the incorporation of interaction variables has not been sufficiently explored. MC-EWS uses a machine learning approach and incorporates nursing assessments and clinically relevant interactions as predictors to achieve a higher predictive accuracy.
The target audience for this activity is professionals and students interested in health informatics.
The general learning objective for all of the JAMIA Journal Club webinars is that participants will
- Use a critical appraisal process to assess article validity and to gauge article findings' relevance to practice
After participating in this webinar, the listener should be able to:
- Measure the accuracy of predictive models using clinically-relevant metrics
- Incorporate clinically-relevant interaction variables into machine learning prediction models
- Recognize the predictive value of nursing assessments for the prediction of inpatient deterioration
The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.
Credit Designation Statement
The American Medical Informatics Association designates this live activity for a maximum of 1 AMA PRA Category 1
Credit(s)™. Physicians should claim only the credit commensurate with the extent of their
participation in the activity.
The live webinar only offers CME credit. The recording on our website will be openly available for learners but will not offer CME credit.
No commercial support was received for this activity.
Disclosures for this Activity
Santiago Romero-Brufau discloses that he receives an IP License Royalty from Jvion, Inc.
The following presenters, planners, and staff who are in a position to control the content of this activity disclose that they and their life partners have no relevant financial relationships with commercial interests:
JAMIA Journal Club planners: Hannah Burkhardt, Kirk E. Roberts, Yuqi Si
AMIA Staff: Susanne Arnold, Pesha Rubinstein
For questions about webinar access, email Susanne@amia.org.
Instructions for Claiming CME Credit
Use the link in the webinar’s chat area to claim credit; in a day or two you will receive an email with your
If you require a certificate of participation, contact Pesha@amia.org.