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Predicting Hospital Length Of Stay In Intensive Care Unit, Namita Singh May 2019

Predicting Hospital Length Of Stay In Intensive Care Unit, Namita Singh

Theses and Dissertations

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 …


Predicting Hospital Length Of Stay In Intensive Care Unit, Namita Singh May 2019

Predicting Hospital Length Of Stay In Intensive Care Unit, Namita Singh

Theses and Dissertations

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 …


A Hierarchical, Fuzzy Inference Approach To Data Filtration And Feature Prioritization In The Connected Manufacturing Enterprise, Phillip Matthew Lacasse Apr 2019

A Hierarchical, Fuzzy Inference Approach To Data Filtration And Feature Prioritization In The Connected Manufacturing Enterprise, Phillip Matthew Lacasse

Theses and Dissertations

The current big data landscape is one such that the technology and capability to capture and storage of data has preceded and outpaced the corresponding capability to analyze and interpret it. This has led naturally to the development of elegant and powerful algorithms for data mining, machine learning, and artificial intelligence to harness the potential of the big data environment. A competing reality, however, is that limitations exist in how and to what extent human beings can process complex information. The convergence of these realities is a tension between the technical sophistication or elegance of a solution and its transparency …