Operating and Managing Street Outreach Services

Increasingly, cities have added street outreach to the mix of strategies used in comprehensive gang reduction efforts, drawing upon mounting evidence of impact. Street outreach relies on street workers to support and advocate on behalf of gang members, or those at high risk of joining gang, to change behavior patterns and link them to needed services and institutions. Street outreach workers work day and night to link marginalized and hard-to-serve individuals in communities with high levels of gang activity to social services, and play an important role in diffusing and stopping violence (Decker, Bynum, McDevitt, Farrell, & Varano, 2008; Spergel, 1966; Office of Juvenile Justice and Delinquency Prevention [OJJDP], 2002). These workers reach out to targeted community members at their homes, community events, on street corners, in parks, and in any neighborhood spaces where community members in gangs or at risk of joining gangs spend time (OJJDP, 2002, p. 54). Outreach workers often possess intimate familiarity with the communities in which they work. Their knowledge and skills allow them to work with individuals whom traditional service providers cannot access or support. California Cities Gang Prevention Network cities (the Network or CCGPN) note that street outreach services are an important piece of their cities' primary intervention strategies, with ties to prevention and enforcement. This bulletin identifies ways outreach programs can strategically support, care for, and hire outreach workers

Faith-Based Institutions and High-Risk Youth

Many of the highest-risk youth in poor communities are not reached by traditional youth programs, but are served by churches and other faith-based institutions that are both well-established and seriously concerned about the welfare of these vulnerable youth and their families. This report, the first in a series from P/PV's National Faith-Based Initiative for High-Risk Youth, provides an initial overview of strategies employed by faith-based institutions in 11 cities, including lessons learned about the distinct contributions of faith-based institutions to the work of civil society, and the challenges of building partnerships between faith-based groups and other institutions -- law enforcement and juvenile justice agencies, foundations and philanthropy, local government and community organizations

Defining architectures for recommended systems for medical treatment. A Systematic Literature Review

This paper presents a Systematic Literature Review(SLR) related to recommender system for medical treatment, aswell as analyze main elements that may provide flexible, accurate,and comprehensive recommendations. To do so, a SLR researchmethodology obey. As a result, 12 intelligent recommendersystems related to prescribing medication were classed dependingto specific criteria. We assessed and analyze these medicinerecommender systems and enumerate the challenges. After studyingselected papers, our study concentrated on two researchquestions concerning the availability of medicine recommendersystems for physicians and the features these systems should have.Further research is encouraged in order to build an intelligentrecommender system based on the features analyzed in this work

Neighborhood based computational approaches for the prediction of lncRNA-disease associations

Motivation: Long non-coding RNAs (lncRNAs) are a class of molecules involved in important biological processes. Extensive efforts have been provided to get deeper understanding of disease mechanisms at the lncRNA level, guiding towards the detection of biomarkers for disease diagnosis, treatment, prognosis and prevention. Unfortunately, due to costs and time complexity, the number of possible disease-related lncRNAs verified by traditional biological experiments is very limited. Computational approaches for the prediction of disease-lncRNA associations allow to identify the most promising candidates to be verified in laboratory, reducing costs and time consuming. Results: We propose novel approaches for the prediction of lncRNA-disease associations, all sharing the idea of exploring associations among lncRNAs, other intermediate molecules (e.g., miRNAs) and diseases, suitably represented by tripartite graphs. Indeed, while only a few lncRNA-disease associations are still known, plenty of interactions between lncRNAs and other molecules, as well as associations of the latters with diseases, are available. A first approach presented here, NGH, relies on neighborhood analysis performed on a tripartite graph, built upon lncRNAs, miRNAs and diseases. A second approach (CF) relies on collaborative filtering; a third approach (NGH-CF) is obtained boosting NGH by collaborative filtering. The proposed approaches have been validated on both synthetic and real data, and compared against other methods from the literature. It results that neighborhood analysis allows to outperform competitors, and when it is combined with collaborative filtering the prediction accuracy further improves, scoring a value of AUC equal to 0966

Towards Integration of Artificial Intelligence into Medical Devices as a Real-Time Recommender System for Personalised Healthcare:State-of-the-Art and Future Prospects

In the era of big data, artificial intelligence (AI) algorithms have the potential to revolutionize healthcare by improving patient outcomes and reducing healthcare costs. AI algorithms have frequently been used in health care for predictive modelling, image analysis and drug discovery. Moreover, as a recommender system, these algorithms have shown promising impacts on personalized healthcare provision. A recommender system learns the behaviour of the user and predicts their current preferences (recommends) based on their previous preferences. Implementing AI as a recommender system improves this prediction accuracy and solves cold start and data sparsity problems. However, most of the methods and algorithms are tested in a simulated setting which cannot recapitulate the influencing factors of the real world. This review article systematically reviews prevailing methodologies in recommender systems and discusses the AI algorithms as recommender systems specifically in the field of healthcare. It also provides discussion around the most cutting-edge academic and practical contributions present in the literature, identifies performance evaluation matrices, challenges in the implementation of AI as a recommender system, and acceptance of AI-based recommender systems by clinicians. The findings of this article direct researchers and professionals to comprehend currently developed recommender systems and the future of medical devices integrated with real-time recommender systems for personalized healthcare

The Users' Perspective on the Privacy-Utility Trade-offs in Health Recommender Systems

Privacy is a major good for users of personalized services such as recommender systems. When applied to the field of health informatics, privacy concerns of users may be amplified, but the possible utility of such services is also high. Despite availability of technologies such as k-anonymity, differential privacy, privacy-aware recommendation, and personalized privacy trade-offs, little research has been conducted on the users' willingness to share health data for usage in such systems. In two conjoint-decision studies (sample size n=521), we investigate importance and utility of privacy-preserving techniques related to sharing of personal health data for k-anonymity and differential privacy. Users were asked to pick a preferred sharing scenario depending on the recipient of the data, the benefit of sharing data, the type of data, and the parameterized privacy. Users disagreed with sharing data for commercial purposes regarding mental illnesses and with high de-anonymization risks but showed little concern when data is used for scientific purposes and is related to physical illnesses. Suggestions for health recommender system development are derived from the findings.Comment: 32 pages, 12 figure

INTEGRATION OF INTERNET OF THINGS AND HEALTH RECOMMENDER SYSTEMS

The Internet of Things (IoT) has become a part of our lives and has provided many enhancements to day-to-day living. In this project, IoT in healthcare is reviewed. IoT-based healthcare is utilized in remote health monitoring, observing chronic diseases, individual fitness programs, helping the elderly, and many other healthcare fields. There are three main architectures of smart IoT healthcare: Three-Layer Architecture, Service-Oriented Based Architecture (SoA), and The Middleware-Based IoT Architecture. Depending on the required services, different IoT architecture are being used. In addition, IoT healthcare services, IoT healthcare service enablers, IoT healthcare applications, and IoT healthcare services focusing on Smartwatch are presented in this research. Along with IoT in smart healthcare, Health Recommender Systems integration with IoT is important. Main Recommender Systems including Content-based filtering, Collaborative-based filtering, Knowledge-based filtering, and Hybrid filtering with machine learning algorithms are described for the Health Recommender Systems. In this study, a framework is presented for the IoT-based Health Recommender Systems. Also, a case is investigated on how different algorithms can be used for Recommender Systems and their accuracy levels are presented. Such a framework can help with the health issues, for example, risk of going to see the doctor during pandemic, taking quick actions in any health emergencies, affordability of healthcare services, and enhancing the personal lifestyle using recommendations in non-critical conditions. The proposed framework can necessitate further development of IoT-based Health Recommender Systems so that people can mitigate their medical emergencies and live a healthy life

Progress and opportunities in lesbian, gay, bisexual and transgender health communications

This article describes elements of effective health communication and highlights strategies that may best be adopted or adapted in relation to lesbian, gay, bisexual, and transgender (LGBT) populations. Studies have documented the utility of multidimensional approaches to health communication from the macro level of interventions targeting entire populations to the micro level of communication between health care provider and consumer. Although evidence of health disparities in LGBT communities underscores the importance of population-specific interventions, health promotion campaigns rarely target these populations and health communication activities seldom account for the diversity of LGBT communities. Advances in health communication suggest promising direction for LGBT-specific risk prevention and health promotion strategies on community, group, and provider/consumer levels. Opportunities for future health communication efforts include involving LGBT communities in the development of appropriate health communication campaigns and materials, enhancing media literacy among LGBT individuals, supporting LGBT-focused research and evaluation of health communication activities, and ensuring that health care providers possess the knowledge, skills, and competency to communicate effectively with LGBT consumers