Semester of Graduation
Spring 2019
Degree
Master of Science in Computer Science (MSCS)
Department
Computer Science
Document Type
Thesis
Abstract
The primary focus of this paper is the creation of a Machine Learning based algorithm for the analysis of large health based data sets. Our input was extracted from MIMIC-III, a large Health Record database of more than 40,000 patients. The main question was to predict if a patient will have complications during certain specified procedures performed in the hospital. These events are denoted by the icd9 code 996 in the individuals' health record. The output of our predictive model is a binary variable which outputs the value 1 if the patient is diagnosed with the specific complication or 0 if the patient is not. Our prediction algorithm is based on a Neural Network architecture, with a 90%-10% training-testing ratio. Our preliminary analysis yielded a prediction accuracy above 80%, outperforming various multi-linear models. A comparative analysis of various optimizers as well as time based performance measures is also included.
Date
12-25-2018
Recommended Citation
Mohan, Namratha, "Predicting Post-Procedural Complications Using Neural Networks on MIMIC-III Data" (2018). LSU Master's Theses. 4840.
https://repository.lsu.edu/gradschool_theses/4840
Committee Chair
Busch, Konstantin
DOI
10.31390/gradschool_theses.4840
Included in
Artificial Intelligence and Robotics Commons, Databases and Information Systems Commons, Other Computer Sciences Commons