Date of Award

May 2019

Degree Type

Thesis

Degree Name

Master of Science

Department

Computer Science

First Advisor

Rohit J Kate

Committee Members

Zeyun Yu, Jun Zhang, Rohit Kate

Keywords

machine learning, predictive analytics

Abstract

In this thesis, we investigate the performance of a series of classification methods for the

Prediction of the hospital Length of Stay (LoS) in Intensive Care Unit (ICU). Predicting

LOS for an inpatient in an hospital is a challenging task but is essential for the operational

success of a hospital. Since hospitals are faced with severely limited resources including

beds to hold admitted patients, prediction of LoS will assist the hospital staff for better

planning and management of hospital resources. The goal of this project is to create a

machine learning model that predicts the length-of stay for each patient at the time of

admission.

MIMIC-III database has been used for this project due to detailed information it contains

about ICU stays. MIMIC is an openly available dataset developed by the MIT Lab for

Computational Physiology, comprising de-identified health data associated with ~40,000

critical care patients at Beth Israel Deaconess Medical Centre. It includes demographics,

vital signs, laboratory tests, medications, and more.

Different machine learning techniques/classifiers have been investigated in this thesis. We

experimented with regression models as well as classification models with different classes

of varying granularity as target for LoS prediction. It turned out that granular classes (in

small unit of days) work better than regression models trying to predict exact duration in

days and hours. The overall performance of our classifiers was ranging from fair to very

good and has been discussed in the results. Secondly, we also experimented with building

separate LoS prediction models built for patients with different disease conditions and

compared it to the joint model built for all patients.

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