Spartan Daily, September 5, 2018

Volume 151, Issue 7https://scholarworks.sjsu.edu/spartan_daily_2018/1049/thumbnail.jp

Unveiling Real-Life Effects of Online Photo Sharing

Social networks give free access to their services in exchange for the right to exploit their users' data. Data sharing is done in an initial context which is chosen by the users. However, data are used by social networks and third parties in different contexts which are often not transparent. In order to unveil such usages, we propose an approach which focuses on the effects of data sharing in impactful real-life situations. Focus is put on visual content because of its strong influence in shaping online user profiles. The approach relies on three components: (1) a set of visual objects with associated situation impact ratings obtained by crowdsourcing, (2) a corresponding set of object detectors for mining users' photos and (3) a ground truth dataset made of 500 visual user profiles which are manually rated per situation. These components are combined in LERVUP, a method which learns to rate visual user profiles in each situation. LERVUP exploits a new image descriptor which aggregates object ratings and object detections at user level and an attention mechanism which boosts highly-rated objects to prevent them from being overwhelmed by low-rated ones. Performance is evaluated per situation by measuring the correlation between the automatic ranking of profile ratings and a manual ground truth. Results indicate that LERVUP is effective since a strong correlation of the two rankings is obtained. A practical implementation of the approach in a mobile app which raises user awareness about shared data usage is also discussed

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

Predictive Analysis on Twitter: Techniques and Applications

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories

Predictive Modelling Approach to Data-driven Computational Psychiatry

This dissertation contributes with novel predictive modelling approaches to data-driven computational psychiatry and offers alternative analyses frameworks to the standard statistical analyses in psychiatric research. In particular, this document advances research in medical data mining, especially psychiatry, via two phases. In the first phase, this document promotes research by proposing synergistic machine learning and statistical approaches for detecting patterns and developing predictive models in clinical psychiatry data to classify diseases, predict treatment outcomes or improve treatment selections. In particular, these data-driven approaches are built upon several machine learning techniques whose predictive models have been pre-processed, trained, optimised, post-processed and tested in novel computationally intensive frameworks. In the second phase, this document advances research in medical data mining by proposing several novel extensions in the area of data classification by offering a novel decision tree algorithm, which we call PIDT, based on parameterised impurities and statistical pruning approaches toward building more accurate decision trees classifiers and developing new ensemblebased classification methods. In particular, the experimental results show that by building predictive models with the novel PIDT algorithm, these models primarily led to better performance regarding accuracy and tree size than those built with traditional decision trees. The contributions of the proposed dissertation can be summarised as follow. Firstly, several statistical and machine learning algorithms, plus techniques to improve these algorithms, are explored. Secondly, prediction modelling and pattern detection approaches for the first-episode psychosis associated with cannabis use are developed. Thirdly, a new computationally intensive machine learning framework for understanding the link between cannabis use and first-episode psychosis was introduced. Then, complementary and equally sophisticated prediction models for the first-episode psychosis associated with cannabis use were developed using artificial neural networks and deep learning within the proposed novel computationally intensive framework. Lastly, an efficient novel decision tree algorithm (PIDT) based on novel parameterised impurities and statistical pruning approaches is proposed and tested with several medical datasets. These contributions can be used to guide future theory, experiment, and treatment development in medical data mining, especially psychiatry

Rehabilitation research project: towards a blueprint for opiate addicts in the Eastern Health Board area.

This document presented the findings of a research project commissioned by the Drug Rehabilitation Committee of the Eastern Health Board. The research was carried out in order to facilitate the informed planning of rehabilitation services in the future. The report presented three distinct perspectives: those of the clients, of the staff, and of community groups. Ninety-four opiate addicts, attending rehabilitation services funded and/or operated by the Eastern Health Board, were interviewed. Focus groups were held for staff in drugs services in each of the Eastern Health Boardís three areas, and for those in local drug task forces, local area projects and community organisations. In drawing their conclusions, the authors drew all three perspectives together. Rehabilitation, the authors recommended, should be comprehensive. It should be client-centred, taking into account all aspects of a clientís life. Delivered in the context of an integrated multi-disciplinary service, it should offer a range of responses and be well resourced with fast access. The place of methadone in rehabilitation services was questioned. Public education programmes were also emphasised, and it was recommended that recovered addicts be enabled to play a part in designing and delivering services

Environmental and genetic correlates of neuropsychiatric diseases and the role of erythropoietin/hypoxia in the brain as potential treatment targets

Neuropsychiatric disorders are relatively frequent and present a considerable burden to affected individuals and society. Disease etiology is often complex and patients exhibit large heterogeneity regarding disease causing factors, presentation and progression. Available treatment options are often ineffective and/or linked to unwanted side-effects. On the other hand, availability of successful preventive actions is limited and requires a detailed study of risk factors for neuropsychiatric disease and its specific symptoms. The first part of this thesis aimed to investigate environmental and genetic risk factors for polytoxicomania, i.e. multiple drug (ab)use, as a frequent comorbidity of schizophrenia. In a sample of $\sim$2000 schizophrenia/schizoaffective patients, we addressed the question if the exposure to accumulated environmental risk in early life increases susceptibility to polytoxicomania. Indeed, the accumulation of environmental risk was strongly associated with polytoxicomania throughout life and in particular in pre-adulthood. Moreover, the development of a novel GWAS-PGAS approach led to the identification of 41 common genetic variants potentially conferring risk to preadult polytoxicomania. The objective of the second part of this thesis work was to further investigate brain-directed effects of hypoxia and erythropoietin (EPO) - a central hypoxia-inducible gene - as potential treatment option for neuropsychiatric disorders. Using a hypoxia reporter mouse line (CAG-CreERT2-ODD::R26R-tdTomato), we showed that both inspiratory hypoxia and motor-cognitive challenge, which causes endogenous hypoxia as a result of extensive neuronal activation (termed "functional hypoxia"), increased the number of hypoxic cells in the brain and the expression of hypoxia-inducible genes in the hippocampus. Interestingly, cell types showed variable responsivity to hypoxia: While neurons and endothelial cells were frequently labelled, hypoxia-labelling in microglia was entirely absent. Technical artifacts explaining this phenomenon were excluded by comparing construct mRNA levels across all cell types. Hexokinase 2 (Hk2) was identified as a mediator of cell type-specific hypoxia responsivity. In addition, we report rapid effects of EPO on adult neurodifferentiation in the CA1 (6 hours after injection). Enhanced neuronal differentiation continued under EPO treatment, driving immature neurons (\textit{Tbr1}+, \textit{Tle4}+ and later \textit{Zbtb20}+) towards maturity, and resulted in $\sim$20\% more neurons in the CA1 after 3 weeks of treatment. Simultaneously, the number of microglia in this region declined by an initial wave of apoptosis, followed by attenuated proliferation. This reduction was necessary for the increase in new neurons. Expression data further indicated that the microglial-neuron balance was maintained by signalling of microglial Colony-Stimulating Factor 1 Receptor (CSF1R) and its neuronally expressed ligand Interleukin 34 (IL34).2022-06-0

Addiction through the Ages: a review of the development of concepts and ideas about addiction in European countries since the nineteenth century and the role of international organisations in the process

The work on addiction through the ages proceeded through 4 interlinked studies: The emergence of concepts of addiction across Europe at the national level, 1860-1980 The framing of the alcohol question at the international alcohol conferences The role of the World Health Organisation (WHO) and its expert committees in defining addiction from the 1940s to the early twenty first century The role of the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) in defining concepts of addiction The long view of addiction concepts at the country level over time points to continuities and changes across countries. They have also played a significant role in international organisations, the pre World War Two alcohol conferences and the World Health Organisation (WHO) after that war. The European level through the European Monitoring Centre on Drugs and Drug Addiction has also come into the picture in more recent times with discussion of a different set of concepts. Although a degree of stability has been achieved around addiction concepts, these still encapsulate a variety of meanings which translate into different treatment and policy approaches and traditions within Europe. By understanding the history of such concepts and how and why they came in and out of use, we can better understand addiction terminology and substance use policy today