Improving prediction of opioid use disorder with machine learning algorithms

Title from PDF of title page, viewed August 9, 2023Thesis advisor: Farid Nait-AbdesselamVitaIncludes bibliographical references (pages 47-53)Thesis (M.S.)--Department of Computer Science and Electrical Engineering. University of Missouri--Kansas City, 2023Opioid Use Disorder (OUD) refers to a concerning pattern of opioid use that results in considerable impairment or distress. It is a chronic and treatable condition that can impact individuals irrespective of their race, gender, income, or social standing. Therefore, accurately predicting individuals at high risk of developing opioid use disorder is of utmost significance. Extensive research has been undertaken to forecast opioid use disorder and determine the most effective predictors that can enhance accuracy levels. This research proposes an improvement to these studies by utilizing two feature selection techniques, Boruta and Recursive Feature Elimination (RFE), to identify the best predictor through a majority voting system. The selected feature, combined with 10 demographic, socioeconomic, physical, and psychological predictors (gender, age, employment Status (Full time, Part time,..), income, Level of the impact of the disorder on an adults life in 4 role domains (home management, work, social life, close relationships with other, education, age when first drink alcoholic beverage, any mental illness, age when first used Marijuana, overall health), resulted in better prediction accuracy using various machine learning classification algorithms. To build a labeled dataset, responses from the 2018 and 2019 edition of the National Survey on Drug Use and Health (NSDUH) were collected. This dataset was used to train and test several classification models (Artificial Neural Network, Naïve Bayes and XgBoost), with the average recall, precision, f1 score, accuracy and AUC compared using 50 iterations. The results showed that considering “psychotherapeutic dependence or abuse” or “illicit drug other than marijuana dependence or abuse” have significant impact on improving accuracy metrics of used machine learning algorithms. Both Xgboost and naïve bayes was able to predict patients at risk for OUD with an AUC of 0.995. This work can aid healthcare providers in determining appropriate preventive care and resources for at-risk patients.Introduction -- Background and related work -- Proposed work -- Results and evaluations -- Conclusion and future wor

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace

Tune your brown clustering, please

Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly unexplored. Accordingly, we present information for practitioners on the behaviour of Brown clustering in order to assist hyper-parametre tuning, in the form of a theoretical model of Brown clustering utility. This model is then evaluated empirically in two sequence labelling tasks over two text types. We explore the dynamic between the input corpus size, chosen number of classes, and quality of the resulting clusters, which has an impact for any approach using Brown clustering. In every scenario that we examine, our results reveal that the values most commonly used for the clustering are sub-optimal