Brown Digital Repository | Item

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Overview

Year:: 2017
Contributor:: Aluthge, Dilum (creator); Sinha, Ishan (creator); Kleiner, Justin (creator); Chen, Elizabeth (creator); Sarkar, Indra Neil (creator)
Genre:: posters
Subject:: Machine learning; Stroke

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Description

Abstract:: Risk calculators for predicting mortality in stroke patients can be used to identify which patients require additional attention and could ultimately result in fewer deaths and more efficient use of resources. We present a comparison of classical statistics and machine learning models for predicting in-hospital mortality following stroke. Our results demonstrate that the studied approaches have complementary performance and therefore have the potential to be used in combination to predict mortality.
Notes:: Scholarly concentration: Biomedical Informatics; All rights reserved

Content

Citation

Aluthge, Dilum, Sinha, Ishan, Kleiner, Justin, et al., "Comparison of Machine Learning and Traditional Statistical Models for Prediction of In-Hospital Mortality Following Stroke" (2017). Warren Alpert Medical School Academic Symposium. Brown Digital Repository. Brown University Library. https://repository.library.brown.edu/studio/item/bdr:698147/

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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 …
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Comparison of Machine Learning and Traditional Statistical Models for Prediction of In-Hospital Mortality Following Stroke