Description
- Abstract:
- Surgical site infection (SSI) is a rare, but serious complication for patients undergoing total joint replacements. We aimed to create a machine learning algorithm that could accurately diagnose infection in the 199 patients that had undergone total knee or hip replacements from the MIMIC-III database.1 After data preprocessing, processing, and analysis, we determined that the dataset was too small to produce a meaningful machine learning algorithm. However, we recognize that this study serves as an important framework for studying larger datasets with more patients.
- Notes:
- Scholarly concentration: Biomedical Informatics
- All rights reserved
Citation
Biron, Dustin, Jain, Sukrit, Stey, Paul, et al.,
"Using Machine Learning to Predict Surgical Site Infection"
(2017).
Warren Alpert Medical School Academic Symposium.
Brown Digital Repository. Brown University Library.
https://repository.library.brown.edu/studio/item/bdr:698107/
Relations
Collection:
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Warren Alpert Medical School Academic Symposium
The Warren Alpert Medical School Academic Symposium is an annual event at Warren Alpert Medical School of Brown University that provides Year II medical students a venue to present their summer research in a poster format. Participation in the Symposium …...