Sarmad ZafarFollow

Degree

Master of Science in Computer Science

Department

Department of Computer Science

School

School of Mathematics and Computer Science (SMCS)

Date of Submission

Spring 2022

Supervisor

Dr Tariq Mahmood

Committee Member 1

Dr. Sajjad Haider, Head of Artificial Intelligence Lab and Professor of Computer Science, Institute of Business Administration

Committee Member 2

Dr. Quratulain Nizamuddin Rajput, Assistant Professor, Institute of Business Administration, Karachi

Abstract

At the time of admission, predicting the Length of Stay (LOS) for the hospitalized patient could greatly help in efficient hospital resource utilization. Accurate LoS estimates beforehand are valuable for all stakeholders, including patients, doctors, and hospital administrators. As larger LoS associated with the severity of the illness, in advance LoS estimates could allow early interventions to avoid complications of disease. It also enables hospital management in more efficient utilization of human resources & facilities, resulting in increased patient flow & minimizing nonvalue added care time in hospitals and helping patients with cost estimation. However, making accurate estimations of LoS could be an arduous task. In this study, we developed a non-disease-specific predictive model using machine learning techniques and Bayesian methods for predicting the hospital length of stay based on static inputs, that is, measures that are available at the time of admission. Although many traditional methods which use statistical regression techniques have been used to predict the length of stay in hospitalized patients, but powerful machine learning techniques have not yet been explored much. Applying machine learning (ML) methods that handle multiple diverse inputs could strengthen predictive abilities and improve results. We compare and discuss the performance of various commonly used supervised machine learning algorithms with Bayesian predictive models, which have never been used in literature for predicting the length of stay admitted patients. The models are trained and validated on a dataset from Aga Khan University Hospital pediatric patients admitted to the hospital from 2015 to 2019.

Document Type

Restricted Access

Submission Type

Thesis

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