Title Information

Title

Predicting Mortality in Diabetic ICU Patients Using Machine Learning and Severity Indices

Name: Personal

Name Part

Anand, Rajsavi

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Role Term: Text

creator

Name: Personal

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Stey, Paul

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creator

Name: Personal

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Jain, Sukrit

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creator

Name: Personal

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Biron, Dustin R.

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creator

Name: Personal

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Bhatt, Harikrishna

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creator

Name: Personal

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Monteiro, Kristina

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creator

Name: Personal

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Feller, Edward

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creator

Name: Personal

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Ranney, Megan L.

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creator

Name: Personal

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Sarkar, Indra Neil

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creator

Name: Personal

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Chen, Elizabeth S.

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Role Term: Text

creator

Type of Resource

text

Genre (aat)

posters

Origin Information

Date Created (keyDate="yes", encoding="w3cdtf")

2017

Language

Language Term: Code (ISO639-2B)

eng

Note (displayLabel="Scholarly concentration")

Biomedical Informatics

Note

All rights reserved

Abstract

Diabetes constitutes a significant health problem that leads directly to many long term health problems including renal, cardiovascular, and neuropathic complications that can result in increased health care costs, as well as risk of ICU stay and mortality. Using the MIMIC III database, a Beth Israel ICU database from 2002-2012, machine learning and binomial logistic regression modeling were applied to test numerous predictive algorithms to predict risk of mortality. The final models achieved good fit with strong AUC values of 0.787 and 0.785 respectively. Additionally, this study demonstrated that robust classification can be done as a combination of five variables to predict risk as compared with many other machine learning models that require nearly 35 variables for similar risk assessment and prediction

Subject (Local)

Topic

Predictive modelling