Description
- Abstract:
- Stroke remains a prominent cause of morbidity and disability in our world today. Technologies such as computed tomography (CT) and magnetic resonance imaging (MRI) may be used to diagnose stroke. What is currently unavailable is the capability to make accurate predictions of stroke outcomes, including mortality and disposition. Such information may allow for high-risk patients to be identified and better treatment approaches to be delineated, thus potentially decreasing morbidity and reducing healthcare expenditures1. Machine learning is an approach that may be used in making such a prediction. In this study, we used machine learning to predict stroke mortality using health information gathered from stroke patients from the Medical Information Mart for Intensive Care III (MIMIC-III) database.
- Notes:
- Scholarly concentration: Biomedical Informatics
Access Conditions
- Use and Reproduction
- All rights reserved
Citation
Zhang, Keven, Aluthge, Dilum, and Sinha, Ishan,
"Using a random forest classifier to predict stroke mortality and disposition in ICU patients"
(2018).
Warren Alpert Medical School Academic Symposium.
Brown Digital Repository. Brown University Library.
https://repository.library.brown.edu/studio/item/bdr:833864/
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 …...