Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Rahimi, Shahram

Committee Member

Perkins, Andy D.

Committee Member

Bethel, Cindy

Date of Degree

4-30-2021

Original embargo terms

Worldwide

Document Type

Graduate Thesis - Open Access

Major

Computer Science

Degree Name

Master of Science

College

James Worth Bagley College of Engineering

Department

Department of Computer Science and Engineering

Abstract

As a complicated lethal medical emergency, sepsis is not easy to be diagnosed until it is too late for taking any life saving actions. Early prediction of sepsis in ICUs may reduce inpatient mortality rate. Although deep learning models can make predictions on the outcome of ICU stays with high accuracies, the opacity of such neural networks decreases their reliability. Particularly, in the ICU settings where the time is not on doctors' side and every single mistake increase the chances of patient's mortality. Therefore, it is crucial for the predictive model to provide some sort of reasoning in addition to the prediction it provides, so that the medical staff could avoid actions based on false alarms. To address this problem, we propose to add an attention layer to a deep recurrent neural network that can learn the relative importance of each of the parameters of the multivariate data of the ICU stay. Our approach sheds light on providing explainability through attention mechanism. We compare our method with some of the state-of-the-art methods and show the superiority of our approach in terms of providing explanations.

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