Spotting the diffusion of New Psychoactive Substances over the Internet

Online availability and diffusion of New Psychoactive Substances (NPS) represent an emerging threat to healthcare systems. In this work, we analyse drugs forums, online shops, and Twitter. By mining the data from these sources, it is possible to understand the dynamics of drugs diffusion and their endorsement, as well as timely detecting new substances. We propose a set of visual analytics tools to support analysts in tackling NPS spreading and provide a better insight about drugs market and analysis

Data Mining in Health-Care: Issues and a Research Agenda

While data mining has become a much-lauded tool in business and related fields, its role in the healthcare arena is still being explored. Currently, most applications of data mining in healthcare can be categorized into two areas: decision support for clinical practice, and policy planning/decision making. However, it is challenging to find empirical literature in this area since a substantial amount of existing work in data mining for health care is conceptual in nature. In this paper, we review the challenges that limit the progress made in this area and present considerations for the future of data mining in healthcare

Novel Natural Language Processing Models for Medical Terms and Symptoms Detection in Twitter

This dissertation focuses on disambiguation of language use on Twitter about drug use, consumption types of drugs, drug legalization, ontology-enhanced approaches, and prediction analysis of data-driven by developing novel NLP models. Three technical aims comprise this work: (a) leveraging pattern recognition techniques to improve the quality and quantity of crawled Twitter posts related to drug abuse; (b) using an expert-curated, domain-specific DsOn ontology model that improve knowledge extraction in the form of drug-to-symptom and drug-to-side effect relations; and (c) modeling the prediction of public perception of the drug’s legalization and the sentiment analysis of drug consumption on Twitter. We collected 7.5 million data from August 2015 to March 2016. This work leveraged a longstanding, multidisciplinary collaboration between researchers at the Population & Center for Interventions, Treatment, and Addictions Research (CITAR) in the Boonshoft School of Medicine and the Department of Computer Science and Engineering. In addition, we aimed to develop and deploy an innovative prediction analysis algorithm for eDrugTrends, capable of semi-automated processing of Twitter data to identify emerging trends in cannabis and synthetic cannabinoid use in the U.S. In addition, the study included aim four, a use case study defined by tweets content analyzing PLWH, medication patterns, and identifying keyword trends via Twitter-based, user-generated content. This case study leveraged a multidisciplinary collaboration between researchers at the Departments of Family Medicine and Population and Public Health Sciences at Wright State University’s Boonshoft School of Medicine and the Department of Computer Science and Engineering. We collected 65K data from February 2022 to July 2022 with the U.S.-based HIV knowledge domain recruited via the Twitter API streaming platform. For knowledge discovery, domain knowledge plays a significant role in powering many intelligent frameworks, such as data analysis, information retrieval, and pattern recognition. Recent NLP and semantic web advances have contributed to extending the domain knowledge of medical terms. These techniques required a bag of seeds for medical knowledge discovery. Various initiate seeds create irrelevant data to the noise and negatively impact the prediction analysis performance. The methodology of aim one, PatRDis classifier, applied for noisy and ambiguous issues, and aim two, DsOn Ontology model, applied for semantic parsing and enriching the online medical to classify the data for HIV care medications engagement and symptom detection from Twitter. By applying the methodology of aims 2 and 3, we solved the challenges of ambiguity and explored more than 1500 cannabis and cannabinoid slang terms. Sentiments measured preceding the election, such as states with high levels of positive sentiment preceding the election who were engaged in enhancing their legalization status. we also used the same dataset for prediction analysis for marijuana legalization and consumption trend analysis (Ohio public polling data). In Aim 4, we applied three experiments, ensemble-learning, the RNN-LSM, the NNBERT-CNN models, and five techniques to determine the tweets associated with medication adherence and HIV symptoms. The long short-term memory (LSTM) model and the CNN for sentence classification produce accurate results and have been recently used in NLP tasks. CNN models use convolutional layers and maximum pooling or max-overtime pooling layers to extract higher-level features, while LSTM models can capture long-term dependencies between word sequences hence are better used for text classification. We propose attention-based RNN, MLP, and CNN deep learning models that capitalize on the advantages of LSTM and BERT techniques with an additional attention mechanism. We trained the model using NNBERT to evaluate the proposed model\u27s performance. The test results showed that the proposed models produce more accurate classification results, and BERT obtained higher recall and F1 scores than MLP or LSTM models. In addition, We developed an intelligent tool capable of automated processing of Twitter data to identify emerging trends in HIV disease, HIV symptoms, and medication adherence

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

Social determinants of health and a grounded mixed-methods approach to explain declining life expectancy in Eastern Kentucky : 1980 – 2014.

Trends in life expectancy for the United States from 1980-2014 suggest general improvement overall. However, eastern Kentucky stands out with a cluster of counties experiencing declining life expectancy. Given that life expectancy is an excepted indicator of overall population health, this anomaly in eastern Kentucky warrants investigation. This thesis uses a grounded mixed methods approach to explore this trend across the greater Appalachian region. Content and discourse analysis of interviews with public health and medical professionals in the study area revealed key themes perceived as being related to declining life expectancy, which informed variable selection for regression analysis. Regression models included ordinary least squares, spatial lag and geographically weighted regression (GWR) and were used to explore relationships between key themes from the interviews and declining life expectancy. Results suggest certain causes of death, including cancer, cardiovascular disease, cirrhosis and other liver disease, mental health and substance abuse, and other non-communicable diseases, are statistically significant related to the eastern Kentucky anomaly. Further, overdose mortality rate, average poverty rate, mining/manufacturing jobs and change in college-educated adults stood out as the most powerful explanatory variables. Moreover, GWR revealed nonstationarity in the relationships between life expectancy and the explanatory variables whereby the regression model performed best in eastern Kentucky, but had less explanatory power elsewhere, particularly in the norther Appalachian region. Ultimately, place-based interpretations of health in Appalachia and the mixed methods approach provided deeper insight into life expectancy trends across the Appalachian region and specifically eastern Kentucky were socioeconomic and cultural forces moderate health and engagement with the healthcare system

Kids Count Alaska 2006/2007

About This Year’s Book Every year the Kids Count Alaska data book reports on how the children of Alaska are doing. But we also like to tell readers a bit more about life in Alaska, to help them understand the place Alaska’s children call home. This year, we’re celebrating the wildlife that is so much a part of life in Alaska. Alaskans watch, hunt, photograph, and coexist with hundreds of large and small species of animals and birds. That coexistence is not always easy for either the wildlife or the people, but it is always interesting. An increasing number of tourists are also being drawn to Alaska for the opportunity to see wildlife that is either scarce or non-existent in other areas of the United States and the world. The whimsical wildlife illustrations on the cover and at the start of each indicator section are the work of Sebastian Amaya Garber, a talented young artist who grew up in Alaska but is now working toward a degree in industrial design at Western Washington University in Bellingham, Washington. The flip side of each illustration describes something about the specific animals and birds we’re profiling, which are: The sea otter, whose rich fur brought the Russians to Alaska • in the century before the United States bought Alaska The brown bear, one of the most respected and feared land • animals in North America The raven, which plays a big role in Alaska Native culture and • is one of the smartest, toughest birds anywhere The puffin, whose large, yellow-orange bill and orange feet • make it a stand-out in Alaska’s coastal waters The moose, which can weigh up to 1,500 pounds and is • often seen wandering neighborhoods and crossing streets in Alaska’s largest urban areas The humpback whale, whose dramatic breaches make it a • favorite of Alaskans and visitors along the southern coast of Alaska in the summertime Whahat is Kids Count Alaska? Kids Count Alaska is part of a nationwide program, sponsored by the Annie E. Casey Foundation, to collect and publicize information about children’s health, safety, and economic status. We pull together information from many sources and present it all in one place. We hope this book gives Alaskans a broad picture of how the state’s children are doing and provides parents, policymakers, and others interested in the welfare of children with information they need to improve life for children and families. Our goals are: Broadly distributing information about the status of Alaska’s • children Creating an informed public, motivated to help children• Comparing the status of children in Alaska with children • nationwide, and presenting additional Alaska indicators (including regional breakdowns) when possible Who Are Alaska’s Children? More than 206,000 children ages 18 or younger live in Alaska—just under a third of Alaska’s 2006 population of about 671,000. That’s an increase of about 15% in the number of children since 1990. During the past 15 years the age structure of Alaska children has also changed, with younger children making up a declining share and teenagers a growing share. In 1990, children ages 4 or younger made up 31% of all children; by 2006 that share had dropped to 26%. Among those 15 to 18, the 1990 share was about 16%, but it had risen to 22% by 2006. Boys outnumber girls in Alaska by close to 6%. There are more boys than girls in every age group. Even among infants, boys outnumbered girls by 8% in 2006. Alaska’s children have also grown more racially diverse in the past two decades, as illustrated by the figure showing Alaska’s school children by race. In 1988, 68% of school children were White and 32% were from minorities—primarily Alaska Natives.Wells Fargo. Annie E. Casey Foundation.Introduction / Infancy / Economic Well-Being / Education / Children In Danger / Juvenile Justic

Exploring Text Mining and Analytics for Applications in Public Security: An in-depth dive into a systematic literature review

Text mining and related analytics emerge as a technological approach to support human activities in extracting useful knowledge through texts in several formats. From a managerial point of view, it can help organizations in planning and decision-making processes, providing information that was not previously evident through textual materials produced internally or even externally. In this context, within the public/governmental scope, public security agencies are great beneficiaries of the tools associated with text mining, in several aspects, from applications in the criminal area to the collection of people's opinions and sentiments about the actions taken to promote their welfare. This article reports details of a systematic literature review focused on identifying the main areas of text mining application in public security, the most recurrent technological tools, and future research directions. The searches covered four major article bases (Scopus, Web of Science, IEEE Xplore, and ACM Digital Library), selecting 194 materials published between 2014 and the first half of 2021, among journals, conferences, and book chapters. There were several findings concerning the targets of the literature review, as presented in the results of this article