Date of Award
2020
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
Abstract
Substance misuse is a major problem in the world. in 2014, as many as 52,404 deaths in the US were caused by drug overdoses. in 2001, the monetary cost of drug misuse has been estimated to be 414 billion dollars. in this work, we explore the use of different machine learning algorithms in the prediction of cocaine misuse using structured and unstructured data found in electronic health records. These records contain various attributes that can help with this prediction, including but not limited to chart text data, previous diagnoses of certain diseases and information about the area the patient lives in. Traditional models using only one kind of data are compared with ensembles and neural networks. Finally, the models are evaluated using the PR AUC metric.
Recommended Citation
Kania, Robert Arnold, "Predicting Drug Misuse Status Using Machine Learning on Electronic Health Records" (2020). Master's Theses. 4336.
https://ecommons.luc.edu/luc_theses/4336
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
Copyright Statement
Copyright © 2020 Robert Arnold Kania
