Discovering Barriers to Opioid Addiction Treatment from Social Media: A Similarity Network-Based Deep Learning Approach

Opioid use disorder (OUD) refers to the physical and psychological reliance on opioids. OUD costs the US healthcare systems $504 billion annually and poses significant mortality risk for patients. Understanding and mitigating the barriers to OUD treatment is a high-priority area. Current OUD treatment studies rely on surveys with low response rate because of social stigma. In this paper, we explore social media as a new data source to study OUD treatments. We develop the SImilarity Network-based DEep Learning (SINDEL) to discover barriers to OUD treatment from the patient narratives and address the challenge of morphs. SINDEL reaches an F1 score of 76.79%. Thirteen types of OUD treatment barriers were identified and verified by domain experts. This study contributes to IS literature by proposing a novel deep-learning-based analytical approach with impactful implications for health practitioners

Using Social Media to Combat Opioid Epidemic

Opioid addiction has become one of the largest and deadliest epidemics in the United States. To combat such deadly epidemic, there is an urgent need for novel tools and methodologies to gain new insights into the behavioral processes of opioid abuse and addiction. The role of social media in biomedical knowledge mining has turned into increasingly significant in recent years. The data from social media may contribute information beyond the knowledge of domain professionals (e.g., psychiatrists and epidemics researchers) and could potentially assist in sharpening our understanding toward the behavioral process of opioid addiction and treatment. In this thesis, we propose a novel framework to automate the analysis of social media (i.e., Twitter) for the detection of the opioid users. To model the Twitter users and posted tweets as well as their rich relationships, we constructed a structured heterogeneous information network (HIN) for representation. We then introduce a meta-path-based approach to characterize the semantic relatedness over users. As different meta-paths depict the relatedness over users at different views, we used Laplacian scores to aggregate different similarities formulated by different meta-paths and then a transductive classification model was built to make predictions. We conduct a comprehensive experimental study based on the real sample collections from Twitter to validate the effectiveness of our proposed approach. To improve the performance of automatic opioid user detection, we presented a meta-structure-based method to depict relatedness and integrate content-based similarity to formulate a similarity measure over users. We then aggregate different similarities using multi-kernel learning for opioid user detection. Comprehensive experimental results on real sample collections from Twitter demonstrate the effectiveness of our proposed learning models

Article Search Tool and Topic Classifier

This thesis focuses on 3 main tasks related to Document Recommendations. The first approach deals with applying existing techniques on Document Recommendations using Doc2Vec. A robust representation of the same is presented to understand how noise induced in the embedding space affects predictions of the recommendations. The next phase focuses on improving the above recommendations using a Topic Classifier. A Hierarchical Attention Network is employed for this purpose. In order to increase the accuracy of prediction, this work establishes a relation to embedding size of the words in the article. In the last phase, model-agnostic Explainable AI (XAI) techniques are implemented to prove the findings in this thesis. XAI techniques are also employed to show how we can fine tune model hyper-parameters for a black-box model

Genetic and environmental prediction of opioid cessation using machine learning, GWAS, and a mouse model

The United States is currently experiencing an epidemic of opioid use, use disorder, and overdose-related deaths. While studies have identified several loci that are associated with opioid use disorder (OUD) risk, the genetic basis for the ability to discontinue opioid use has not been investigated. Furthermore, very few studies have investigated the non-genetic factors that are predictive of opioid cessation or their predictive ability. In this thesis, I studied a novel phenotype–opioid cessation, defined as the time since last use of illicit opioids (1 year ago as cease) among persons meeting lifetime DSM-5 criteria for opioid use disorder (OUD). In chapter two, I identified novel genetic variants and biological pathways that potentially regulate opioid cessation success through a genome wide study, as well as genetic overlap between opioid cessation and other substance cessation traits. In chapter three, I identified multiple non-genetic risk factors specific to each racial group that are predictive of opioid cessation from the same individuals analyzed in chapter two by applying several linear and non-linear machine learning techniques to a set of more than 3,000 variables assessed by a structured psychiatric interview. Factors identified from this atheoretical approach can be grouped into opioid use activities, other drug use, health conditions, and demographics, while the predictive accuracy as high as nearly 80% was achieved. The findings from this research generated more hypotheses for future studies to reference. In chapter four, I performed differential gene expression and network analysis on mice with different oxycodone (an opioid receptor agonist)-induced behaviors and compared the significantly associated genes and network modules with top-ranked genes identified in humans. The pathway cross-talks and gene homologs identified from both species illuminate the potential molecular mechanism of opioid behaviors. In summary, this thesis utilized statistical genetics, machine learning, and a computational biology framework to address factors that are associative with opioid cessation in humans, and cross-referenced the genetic findings in a mouse model. These findings serve as references for future studies and provide a framework for personalizing the treatment of OUD

Text Mining for Social Harm and Criminal Justice Applications

Indiana University-Purdue University Indianapolis (IUPUI)Increasing rates of social harm events and plethora of text data demands the need of employing text mining techniques not only to better understand their causes but also to develop optimal prevention strategies. In this work, we study three social harm issues: crime topic models, transitions into drug addiction and homicide investigation chronologies. Topic modeling for the categorization and analysis of crime report text allows for more nuanced categories of crime compared to official UCR categorizations. This study has important implications in hotspot policing. We investigate the extent to which topic models that improve coherence lead to higher levels of crime concentration. We further explore the transitions into drug addiction using Reddit data. We proposed a prediction model to classify the users’ transition from casual drug discussion forum to recovery drug discussion forum and the likelihood of such transitions. Through this study we offer insights into modern drug culture and provide tools with potential applications in combating opioid crises. Lastly, we present a knowledge graph based framework for homicide investigation chronologies that may aid investigators in analyzing homicide case data and also allow for post hoc analysis of key features that determine whether a homicide is ultimately solved. For this purpose we perform named entity recognition to determine witnesses, detectives and suspects from chronology, use keyword expansion to identify various evidence types and finally link these entities and evidence to construct a homicide investigation knowledge graph. We compare the performance over several choice of methodologies for these sub-tasks and analyze the association between network statistics of knowledge graph and homicide solvability

Opioid Misuse Detection in Hospitalized Patients Using Convolutional Neural Networks

Opioid misuse is a major public health problem in the world. In 2016, 11.3 million people were reported to misuse opioids in the US only. Opioid-related inpatient and emergency department visits have increased by 64 percent and the rate of opioid-related visits has nearly doubled between 2009 and 2014. It is thus critical for healthcare systems to detect opioid misuse cases. Patients hospitalized for consequences of their opioid misuse present an opportunity for intervention but better screening and surveillance methods are needed to guide providers. The current screening methods with self-report questionnaire data are time-consuming and difficult to perform in hospitalized patients. In this work, I explore the use of convolutional neural networks for detecting opioid misuse cases using the text of electronic health records as input. The performance of these models is compared to the performance of a more traditional logistic regression model. Different architectures of a convolutional neural network were trained and evaluated using the area under the ROC curve. A convolutional neural network performed better by producing a score of 93.4% whereas the score produced by logistic regression was 91.4% on the test data. Different advantages and disadvantages of using a convolutional neural network over the baseline logistic regression model were also discussed

Machine Learning techniques applied to the consumption of illegal psychoactive substances: A systematic mapping.

The consumption of illicit psychoactive substances is an issue that is experienced daily, involving people of different ages. It is worth noting that many of these substances can cause disorders, such as:Marijuana or cannabis: Its consumption directly affects brain function, particularly the parts of the brain responsible for memory, learning, attention, and decision-making. Bazuco: It is a toxic substance with significant risks for neurological and bodily deterioration. It dissolves rapidly in the bloodstream, making it highly addictive. Cocaine: Its consumption directly affects the nervous system and the rest of the body immediately. Its effects include vasoconstriction, dilated pupils, hyperthermia, rapid heartbeat, and hypertension. Heroin: It is a highly addictive substance that initially produces pleasurable effects, leading to continued and repetitive use. It also causes dry mouth, flushed skin, heaviness in the limbs, nausea, vomiting, intense itching, and mental impairment. Furthermore, in Colombia, this issue is particularly prominent among young people, depending on the context they find themselves in. Nowadays, there is easy access to these types of substances. As a result, several works have been proposed to address this issue using artificial intelligence. In this regard, the present study reviews 50 publications related to the use of machine learning (ML) methods and techniques applied to the consumption of illicit psychoactive substances. Common themes were found among the included publications, and a summary of the selected articles is provided for each theme. The adopted methods are briefly described, along with a comparison between them, noting the methods used, their results, and other important factors of the application or model in different areas. The study concludes with a series of proposals regarding future research directions in this field.The consumption of illicit psychoactive substances is a problem experienced every day, by people of different ages who have been involved in it, highlighting that many of these substances generate disorders such as, for example: Marijuana or cannabis: its consumption affects brain function directly, and particularly the parts of the brain responsible for memory, learning, attention, decision making. Bazuco: it is a toxic substance, which main risks of consumption are reflected in the neurological deterioration and in the organism, and its dissolution in the bloodstream is very fast, an aspect that makes it very addictive. Cocaine: its consumption, directly affects the nervous system and the rest of the organism immediately, these affectations include vasoconstriction, mydriasis, hyperthermia, tachycardia and hypertension. Heroin: is a highly addictive substance, initially, its effects are very pleasant, which leads to a continuous and repetitive consumption behavior, in addition, it produces sensations of dry mouth, reddening and heating of the skin, heaviness in arms and legs, nausea and vomiting, intense itching and clouding of the mental faculties. On the other hand, in Latin American regions and all over the world, this problem is something that stands out a lot and has a great impact on young people according to the context they are in, since nowadays it is very easy to obtain this type of substances, therefore, a series of works have been proposed that address this problem from artificial intelligence, in this way, the current study is a review of 50 publications related to the use of ML methods and techniques applied to the consumption of illicit psychoactive substances. From the publications included, common themes were found, so a summary is made of the articles selected for each theme and the methods adopted are briefly described, as well as a comparison between them, noting the methods used, their results and other important factors of the application or model in different areas, and concluding with a series of proposals on the lines that could guide future research in this field

Ohio Recovery Housing: Resident Risk and Outcomes Assessment

Addiction and substance abuse disorder is a significant problem in the United States. Over the past two decades, the United States has faced a boom in substance abuse, which has resulted in an increase in death and disruption of families across the nation. The State of Ohio has been particularly hard hit by the crisis, with overdose rates nearly doubling the national average. Established in the mid 1970’s Sober Living Housing is an alcohol and substance use recovery model emphasizing personal responsibility, sober living, and community support. This model has been adopted by the Ohio Recovery Housing organization, which seeks to provide a safe and communal environment to assist in recovering individuals facing substance abuse disorder. As a result of the organization’s efforts, residents in the Ohio Recovery Housing program have seen increased rates of Recovery Capital scores and decreased occurrences of relapse and return to the criminal justice system. A key predictor of positive Ohio Recovery Housing resident outcomes is the length of the program stay. It is crucial that recovery housing residents continue with the program for a minimum of six months, as this time period is associated with reduced rates of recidivism, relapse, and interaction with the criminal justice system. This research explores the use of clustering techniques and predictive modeling to help inform potential risks and factors of treatment disruption before the critical six-month mark of recovery housing support

Recent Changes in Drug Abuse Scenario: The Novel Psychoactive Substances (NPS) Phenomenon

copyright 2019 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.Final Published versio