Intelligent Data Monitoring and Controlling System for Health Related Social Networks

Depression is a worldwide wellbeing concern in view of healthcare. Now a days, social media became popular to allow the affected people to share their experience in the form of posts. These kinds of experiences are stored in the database and extracted and analyzed to give the precautions to the other people or to recall the drugs from the side effects, and other service improvements in their treatment regarding to a particular disease. In such cases depression-related social websites are helpful to monitor or get knowledge in various kinds of drugs, side effects and to share the user experiences. In this paper, we proposed a social media website to allow the users to share the experiences of a particular disease i.e. depression and their experience over on it. We used a weighted network model to represent the activities in the social networks. The proposed work has three steps. The first one is to monitor the user activity and followed by network clustering and the module analysis. The persons who likes a particular post comes under a group and those who contrasted belongs to other group. The stop word technique we have implemented in this work is helpful to avoid the misleading communication over the posts and for the efficient user interaction. The statistical analysis of this kind of user interactions are helpful in health networks to gain much knowledge about a specific disease. This approach will enable all the gatherings to take a part and for the future healthcare improvements to the patients suffering from a disease

Detecting the magnitude of depression in Twitter users using sentiment analysis

Today the different social networking sites have enabled everyone to easily express and share their feelings with people around the world. A lot of people use text for communicating, which can be done through different social media messaging platforms available today such as Twitter, Facebook etc, as they find it easier to express their feelings through text instead of speaking them out. Many people who also suffer from stress find it easier to express their feelings on online platform, as over there they can express themselves very easily. So if they are alerted beforehand, there are ways to overcome the mental problems and stress they are suffering from. Depression stands out to be one of the most well known mental health disorders and a major issue for medical and mental health practitioners. Legitimate checking can help in its discovery, which could be useful to anticipate and prevent depression all-together.Hence there is a need for a system, which can cater to such issues and help the user. The purpose of this paper is to propose an efficient method that can detect the level of depression in Twitter users. Sentiment scores calculated can be combined with different emotions to provide a better method to calculate depression scores. This process will help underscore various aspects of depression that have not been understood previously. The main aim is to provide a sense of understanding regarding depression levels in different users and how the scores can be correlated to the main data

Knowledge Base for MENTAL AI, in Data Science Context

Globally, 1 in 7 people has some kind of mental or substance use disorder that affects their thinking, feelings, and behaviour in everyday life. Mental well-being is vital for physical health. No Health Without Mental Health! People with mental health disorders can carry on with normal life if they get the proper treatment and support. Mental disorders are complex to diagnose due to similar and common symptoms for numerous types of mental illnesses, with a minute difference among them. In the era of big, the challenge stays to make sense of the huge amount of health research and care data. Computational methods hold significant potential to enable superior patient stratification approaches to the established clinical practice, which in turn are a pre-requirement for the development of effective personalized medicine approaches. Personalized psychiatry also plays a vital role in predicting mental disorders and improving diagnosis and optimized treatment. The use of intelligent systems is expected to grow in the medical field, and it will continue to pose abundant opportunities for solutions that can help save patients’ lives. As it does for many industries, Artificial Intelligence (AI) systems can support mental health specialists in their jobs. Machine learning algorithms can be applied to find different patterns in the most diverse sets of data. This work aims to examine and compare different machine learning classification methodologies to predict different mental disorders and, from that, extract knowledge that can help mental health professionals in their tasks. Our algorithms were trained using a total dataset of 3353 patients from different hospital units. These data are divided into three subsets of data, mainly by the characteristics that the pathologies present. We evaluate the performance of the algorithms using different metrics. Among the metrics applied, we chose the F1 score to compare and analyze the algorithms, as it is the most suitable for the data we have since they found themselves imbalances. In the first evaluation, we trained our models, using all the patient’s symptoms and diagnoses. In the second evaluation, we trained our models, using only the symptoms that were somehow related to each other and that influenced the other pathologies.Milhões de pessoas em todo o mundo são afetadas por transtornos mentais que influenciam o seu pensamento, sentimento ou comportamento. A saúde mental é um pré-requisito essencial para a saúde física e geral. Pessoas com transtornos mentais geralmente precisam de tratamento e apoio adequados para levar uma vida normal. A saúde mental é uma condição de bem-estar em que um indivíduo reconhece as suas habilidades, pode lidar com as tensões quotidianas da vida, trabalhar de forma produtiva e pode contribuir para a sua comunidade. A saúde mental afeta a vida das pessoas com transtorno mental, as suas profissões e a produtividade da comunidade. Boa saúde mental e resiliência são essenciais para a nossa saúde biológica, conexões humanas, educação, trabalho e alcançar o nosso potencial. A pandemia do covid-19 impactou significativamente a saúde mental das pessoas, em particular grupos como saúde e outros trabalhadores da linha de frente, estudantes, pessoas que moram sozinhas e pessoas com condições de saúde mental pré-existentes. Além disso, os serviços para transtornos mentais, neurológicos e por uso de substâncias foram significativamente interrompidos. Os transtornos mentais são classificados como de diagnóstico complexo devido à semelhança dos sintomas. Consultas regulares de saúde de pessoas com transtornos mentais graves podem impedir a morte prematura. A dificuldade dos especialistas em diagnosticar é geralmente causada pela semelhança dos sintomas nos transtornos mentais, como por exemplo, transtorno de bordeline e bipolar. Os algoritmos de aprendizado de máquina podem ser aplicados para encontrar diferentes padrões nos mais diversos conjuntos de dados. Este trabalho, visa examinar e comparar diferentes metodologias de classificação de aprendizado de máquina para prever difentes transtornos mentais e disso, extrair conhecimento que possam auxiliar os profissionais da area de saude mental, nas suas tarefas. Os nossos algoritmos, foram treinados utilizando um conjunto total de dados de 3353 pacientes, provenientes de diferentes unidades hospitalares. Esses dados, estão repartidos em três subconjuntos de dados, principalmente, pelas características que as patologias apresentam. Avaliamos o desempenho dos algoritmos usando diferentes métricas. Dentre as métricas aplicadas, escolhemos o F1 score para comparar e analisar os algoritmos, pois é o mais adequado para os dados que possuímos. Visto que eles se encontravam desequilíbrios. Na primeira avaliação, treinamos os nossos modelos, utilizando todos os sintomas e diagnósticos dos pacientes. Na segunda avaliação, treinamos os nossos modelos, utilizando apenas os sintomas que apresentavam alguma relação entre si e que influenciavam nas outras patologias

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

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

Digital Healthcare and Expertise

This open access book explores how expertise about bipolar disorder is performed on American and French digital platforms by combining insights from STS, medical sociology and media studies. It addresses topical questions, including: How do different stakeholders engage with online technologies to perform expertise about bipolar disorder? How does the use of the internet for processes of knowledge evaluation and production allow for people diagnosed with bipolar disorder to reposition themselves in relation to medical professionals? How do cultural markers shape the online performance of expertise about bipolar disorder? And what individualizing or collectivity-generating effects does the internet have in relation to the performance of expertise? The book constitutes a critical and nuanced intervention into dominant discourses which approach the internet either as a quick technological fix or as a postmodern version of Pandora’s box, sowing distrust among people and threatening unified conceptualizations and organized forms of knowledge

Digital Healthcare and Expertise

This open access book explores how expertise about bipolar disorder is performed on American and French digital platforms by combining insights from STS, medical sociology and media studies. It addresses topical questions, including: How do different stakeholders engage with online technologies to perform expertise about bipolar disorder? How does the use of the internet for processes of knowledge evaluation and production allow for people diagnosed with bipolar disorder to reposition themselves in relation to medical professionals? How do cultural markers shape the online performance of expertise about bipolar disorder? And what individualizing or collectivity-generating effects does the internet have in relation to the performance of expertise? The book constitutes a critical and nuanced intervention into dominant discourses which approach the internet either as a quick technological fix or as a postmodern version of Pandora’s box, sowing distrust among people and threatening unified conceptualizations and organized forms of knowledge

Monitoring and Analysis of Novel Psychoactive Substances in Trends Databases, Surface Web and the Deep Web, with Special Interest and Geo-Mapping of the Middle East

BACKGROUND Novel or new psychoactive substances (NPS), also known as designer drugs and research chemicals, represent a relatively recent phenomenon which can be traced back to the last decade or even earlier. The growth of this phenomenon and its electronic trade (e-trade) has been logarithmic and alarming; its aftermaths are not limited to; the economy, individual and public health, or illicit drug trade. The discipline of NPS has been extensively studied since 2010. However, there are still deficits in; data from the Middle East and the developing world including Arabic countries (1), application of data science and inferential hypothesis testing (2), implementation of the principles and theories of social science (3), utilization of experimental designs including randomised controlled trials (RCT) and quasiexperimental studies (4), and ultimately the enactment of real-time web analysis and the realization of tools of knowledge discovery in databases (5). AIM AND OBJECTIVES This study will implement an innovative research approach by combining observational analyses and data science; the aim is to provide generalizable (inferential) data in relation to NPS e-commerce activities on both divisions of the web, surface and deep. The pinnacle objective is to; assess the proportional magnitude of NPS e-commerce activity in the Middle East (1), provide a thorough analysis of the e-vendors on the darknet, both globally and regionally (Middle East) (2), correlate change in trends of e-commerce with time (3), provide recommendations for future studies in relation to the ecommerce activity in the Middle East (4), and to discuss the colossal potential of data mining technologies (5). MATERIALS AND METHODS This dissertation embodies the integrative and combinatorial approach towards the investigation of the e-trade (e-commerce) of NPS; it is made of integrated studies allocated into eleven results chapters. The utilised investigative tools represent a mixed-breed of observational web analytics including; literature review (1), cross-sectional studies and surveys (2, 3), internet snapshots (4), retrospective analyses (5), and critical appraisal (6). These analyses took place in both appendices of the web (surface web and the anonymous deep web); the analyses specifically involved; Google Trends database (1), literature databases (2), drug fora (3), social communication e-media (3), news and media networks (4), Grams search engine of the deep web (5), the darknet and its e-marketplace (6), Alphabay, Agora, Valhalla, Hansa, other dedicated e-markets for NPS e-trade (7). Additional extrapolations were concluded via the use of surveys and e-surveys in a population of medical students from Iraq. The potentials for knowledge discovery in databases (KDD) were also discussed in all chapters. Each chapter was thoroughly investigated via; data science tools (I), inferential statistics and hypothesis testing (II). The latter was dependent on using the Microsoft Excel 2016, the Statistical Package for the Social Sciences (SPSS), and some online tools of data science. RESULTS AND DISCUSSION A systematic review of approximately 600 PubMed-indexed articles of NPS literature showed; attempts of NPS research started to evolve after 2010, almost one-third of the research output (36%) was of relevance to toxicology and analytic chemistry, while reviews and cross-sectional studies were less common (15%, 18%). The analysis of the individual basis of power showed that NPS researchers, legislators, and policymakers are lagging behind, whereas terrorist possesses the highest possible power. Power scores of e-vendors scored highest in the UK, US, and eastern Europe, while being almost absent in the Middle East. The complimentary usage of PubMed, drug fora, and Google Trends was successful in extrapolating the most trending and high-risk NPS; the contribution from the Middle East to incidents of intoxications and fatalities was absent except for Israel. Deep web analysis, including the darknet emarketplace, has shown that the contribution of the Middle East never exceeded 7% of the total etrade, data were limited to; Iran, Israel, Turkey, Afghanistan, Oman, United Arab Emirates, and Saudi Arabia. Other Arabic countries included; Egypt, Morocco, and Algeria. It was interesting to observe the e-vendors of NPS operating in the Middle East were highly involved in e-trade activities in other nations, primarily; the UK, Western Europe and Scandinavia, US, Canada, Australia, and New Zealand. Surveys and internet snapshots unveiled the lack of awareness and very low prevalence of (ab)use of NPS within the selected Iraqi population. Captagon was highly prevalent in the Middle East, unlike NBOMe and octodrine. In summary, the contribution from the Middle East was microscopic when compared to the developed world; it did not exceed 7% of the entire NPS phenomenon e-trade. Similarly, the NPS research in the region of the Middle East can be described to be in its infancy. The overall level-of-evidence of this dissertation is assumed to be of level-2b according to the classification system imposed by the Oxford Center for Evidence-Based Medicine (2009). CONCLUSION The growth of the NPS phenomenon, including the e-commerce and its links to terrorism, are reaching unprecedented levels. Unless some reasonable efforts and ingenious upgrades of the current research methodologies, the NPS trade and e-trade will continue to prevail rendering all its counter-attempts fade into dust; these attempts are not only limited to NPS research but also into; legislative actions, policy planning, and counter-terrorism. Upgrades should affect these front lines; increasing the quality and quantity of studies in developed countries including Middle Eastern and Arabic countries (1), incorporation of efficient use of data science and advanced web analytics (2), compulsory training of data science, biostatistics, and basic neuroscience for all NPS researchers, chemists, and toxicologists (3), validation and incorporation of data mining and real-time analyses (4), inclusion of the rarely-used experimental studies including RCTs, pragmatic RCTs, and animal modelling (5), enhancement and potentiation of internet snapshot techniques (6), and full exploitation of trends databases of the surface web (7). Perhaps, the integration of real-time data mining and data crunching, and inferential data science technique will represent the climax armament to antagonise the alarming e-trade