A 10-year Study of Factors Associated with Alcohol Treatment Use and Non-use in a U.S. Population Sample

Background This study seeks to identify changes in perceived barriers to alcohol treatment and predictors of treatment use between 1991–92 and 2001–02, to potentially help understand reported reductions in treatment use at this time. Social, economic, and health trends during these 10 years provide a context for the study. Methods Subjects were Whites, Blacks, and Hispanics. The data were from the National Longitudinal Alcohol Epidemiologic Survey (NLAES) and the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). We conducted two analyses that compared the surveys on: 1) perceived treatment barriers for subjects who thought they should get help for their drinking, and 2) variables predicting past-year treatment use in an alcohol use disorder subsample using a multi-group multivariate regression model. Results In the first analysis, those barriers that reflected negative beliefs and fears about seeking treatment as well as perceptions about the lack of need for treatment were more prevalent in 2001–02. The second analysis showed that survey year moderated the relationship between public insurance coverage and treatment use. This relationship was not statistically significant in 1991–92 but was significant and positive in 2001–02, although the effect of this change on treatment use was small. Conclusions Use of alcohol treatment in the U.S. may be affected by a number of factors, such as trends in public knowledge about treatment, social pressures to reduce drinking, and changes in the public financing of treatment

Privacy and Accountability in Black-Box Medicine

Black-box medicine—the use of big data and sophisticated machine learning techniques for health-care applications—could be the future of personalized medicine. Black-box medicine promises to make it easier to diagnose rare diseases and conditions, identify the most promising treatments, and allocate scarce resources among different patients. But to succeed, it must overcome two separate, but related, problems: patient privacy and algorithmic accountability. Privacy is a problem because researchers need access to huge amounts of patient health information to generate useful medical predictions. And accountability is a problem because black-box algorithms must be verified by outsiders to ensure they are accurate and unbiased, but this means giving outsiders access to this health information. This article examines the tension between the twin goals of privacy and accountability and develops a framework for balancing that tension. It proposes three pillars for an effective system of privacy-preserving accountability: substantive limitations on the collection, use, and disclosure of patient information; independent gatekeepers regulating information sharing between those developing and verifying black-box algorithms; and information-security requirements to prevent unintentional disclosures of patient information. The article examines and draws on a similar debate in the field of clinical trials, where disclosing information from past trials can lead to new treatments but also threatens patient privacy

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

CASTNet: Community-Attentive Spatio-Temporal Networks for Opioid Overdose Forecasting

Opioid overdose is a growing public health crisis in the United States. This crisis, recognized as "opioid epidemic," has widespread societal consequences including the degradation of health, and the increase in crime rates and family problems. To improve the overdose surveillance and to identify the areas in need of prevention effort, in this work, we focus on forecasting opioid overdose using real-time crime dynamics. Previous work identified various types of links between opioid use and criminal activities, such as financial motives and common causes. Motivated by these observations, we propose a novel spatio-temporal predictive model for opioid overdose forecasting by leveraging the spatio-temporal patterns of crime incidents. Our proposed model incorporates multi-head attentional networks to learn different representation subspaces of features. Such deep learning architecture, called "community-attentive" networks, allows the prediction of a given location to be optimized by a mixture of groups (i.e., communities) of regions. In addition, our proposed model allows for interpreting what features, from what communities, have more contributions to predicting local incidents as well as how these communities are captured through forecasting. Our results on two real-world overdose datasets indicate that our model achieves superior forecasting performance and provides meaningful interpretations in terms of spatio-temporal relationships between the dynamics of crime and that of opioid overdose.Comment: Accepted as conference paper at ECML-PKDD 201

Social Media Text Mining Framework for Drug Abuse: An Opioid Crisis Case Analysis

Social media is considered as a promising and viable source of data for gaining insights into various disease conditions, patients’ attitudes and behaviors, and medications. The daily use of social media provides new opportunities for analyzing several aspects of communication. Social media as a big data source can be used to recognize communication and behavioral themes of problematic use of prescription drugs. Mining and analyzing such media have challenges and limitations with respect to topic deduction and data quality. There is a need for a structured approach to efficiently and effectively analyze social media content related to drug abuse in a manner that can mitigate the challenges surrounding the use of this data source. Following a design science research methodology, the research aims at developing and evaluating a framework for mining and analyzing social media content related to drug abuse in a manner that will mitigate challenges and limitations related to topic deduction and data quality. The framework consists of four phases: Topic Discovery and Detection; Data Collection; Data Preparation and Quality; and Analysis and Results. The topic discovery and detection phase consists of a topic expansion stage for the drug abuse related topics that address the research domain and objectives. The topic expansion is based on different terms related to keywords, categories, and characteristics of the topic of interest and the objective of monitoring. To formalize the process and supporting artifacts, we create an ontology for drug abuse that captures the different categories that exist in the topic expansion and the literature. The data collection phase is characterized by the date range, social media platforms, search keywords, and a set of inclusion/exclusion criteria. The data preparation and quality phase is mainly concerned with obtaining high-quality data to mitigate problems with data veracity. In this phase, we pre-process the collected data then we evaluate the quality of the data, with respect to the terms and objectives of the research topic phase, using a data quality evaluation matrix. Finally, in the data analysis phase, the researcher can choose the suitable analysis approach. We used a combination of unsupervised and supervised machine learning approaches, including opinion and content analysis modeling. We demonstrate and evaluate the applicability of the proposed framework to identify common concerns toward opioid crisis from two perspectives; the addicted users’ perspective and the public’s (non-addicted users) perspective. In both cases, data is collected from twitter using Crimson Hexagon, a social media analytics tool for data collection and analysis. Natural language processing is used for data preparation and pre-processing. Different data visualization techniques such as, word clouds and clustering visualization, are used to form a deeper understanding of the relationships among the identified themes for the selected communities. The results help in understanding concerns of the public and opioid addicts towards the opioid crisis in the United States. Results of this study could help in understanding the problem aspects and provide key input when it comes to defining and implementing innovative solutions/strategies to face the opioid epidemic. From a theoretical perspective, this study highlights the importance of developing and adapting text mining techniques to social media for drug abuse. This study proposes a social media text mining framework for drug abuse research which lead to a good quality of datasets. Emphasis is placed on developing methods for improving the discovery and identification of topics in social media domains characterized by a plethora of highly diverse terms and a lack of commonly available dictionary/language by the community such as in the opioid and drug abuse case. From a practical perspective, automatically analyzing social media users’ posts using machine learning tools can help in understanding the public themes and topics that exist in the recent discussions of online users of social media networks. This could help in developing proper mitigation strategies. Examples of such strategies can be gaining insights from the discussion topics to make the opioid media campaigns more effective in preventing opioid misuse. Finally, the study helps address some of the U.S. Department of Health and Human Services (HHS) five-point strategy by providing a systematic approach that could support conducting better research on addiction and drug abuse and strengthening public health data reporting and collection using social media data