Advancing Racial Equity in Communities: Lessons for Philanthropy

Outlines elements of success and lessons learned from efforts by foundations and nonprofits in five communities to help dismantle the structural racism perpetuating disparities in income, wealth, education, housing, employment, and criminal justice

Realizing an Efficient IoMT-Assisted Patient Diet Recommendation System Through Machine Learning Model

Recent studies have shown that robust diets recommended to patients by Dietician or an Artificial Intelligent automated medical diet based cloud system can increase longevity, protect against further disease, and improve the overall quality of life. However, medical personnel are yet to fully understand patient-dietician’s rationale of recommender system. This paper proposes a deep learning solution for health base medical dataset that automatically detects which food should be given to which patient base on the disease and other features like age, gender, weight, calories, protein, fat, sodium, fiber, cholesterol. This research framework is focused on implementing both machine and deep learning algorithms like, logistic regression, naive bayes, Recurrent Neural Network (RNN), Multilayer Perceptron (MLP), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM). The medical dataset collected through the internet and hospitals consists of 30 patient’s data with 13 features of different diseases and 1000 products. Product section has 8 features set. The features of these IoMT data were analyzed and further encoded before applying deep and machine and learning-based protocols. The performance of various machine learning and deep learning techniques was carried and the result proves that LSTM technique performs better than other scheme with respect to forecasting accuracy, recall, precision, and $F1$ -measures. We achieved 97.74% accuracy using LSTM deep learning model. Similarly 98% precision, 99% recall and $99\%~F1$ -measure for allowed class is achieved, and for not-allowed class precision is 89%, recall score is 73% and $F1$ Measure score is 80%

Hot topics in allergen immunotherapy, 2023: Current status and future perspective

The importance of allergen immunotherapy (AIT) is multifaceted, encompassing both clinical and quality‐of‐life improvements and cost‐effectiveness in the long term. Key mechanisms of allergen tolerance induced by AIT include changes in memory type allergen‐specific T‐ and B‐cell responses towards a regulatory phenotype with decreased Type 2 responses, suppression of allergen‐specific IgE and increased IgG$_{1}$ and IgG$_{4}$, decreased mast cell and eosinophil numbers in allergic tissues and increased activation thresholds. The potential of novel patient enrolment strategies for AIT is taking into account recent advances in biomarkers discoveries, molecular allergy diagnostics and mobile health applications contributing to a personalized approach enhancement that can increase AIT efficacy and compliance. Artificial intelligence can help manage and interpret complex and heterogeneous data, including big data from omics and non‐omics research, potentially predict disease subtypes, identify biomarkers and monitor patient responses to AIT. Novel AIT preparations, such as synthetic compounds, innovative carrier systems and adjuvants, are also of great promise. Advances in clinical trial models, including adaptive, complex and hybrid designs as well as real‐world evidence, allow more flexibility and cost reduction. The analyses of AIT cost‐effectiveness show a clear long‐term advantage compared to pharmacotherapy. Important research questions, such as defining clinical endpoints, biomarkers of patient selection and efficacy, mechanisms and the modulation of the placebo effect and alternatives to conventional field trials, including allergen exposure chamber studies are still to be elucidated. This review demonstrates that AIT is still in its growth phase and shows immense development prospects

Effective Organizational Practices for Middle and High School Grades

At the request of the Accountability Review Council, Research for Action identified effective organizational practices used by better performing schools serving substantial numbers of low income middle and high school students in the School District of Philadelphia. These practices are organized into three spheres: Conditions for Teaching, Student-Centered School Community, and Instructional Program. For each sphere, the report offers broad strategies and specific practices to enact the strategies. Nuanced school case studies show how the practices can work synergistically and coherently in schools to help students succeed

Adaptive Analytics: It’s About Time

This article describes a cooperative research partnership among a large public university, a for-profit private institution and their common adaptive learning platform provider. The focus of this work explored adaptive analytics that uses data the investigators describe as metaphorical “digital learning dust” produced by the platform as a matter of course. The information configured itself into acquired knowledge, growth, baseline status and engagement. Two complimentary models evolved. The first, in the public university, captured end-of-course data for predicting success. The second approach, in the private university, formed the basis of a dynamic real-time data analytic algorithm. In both cases the variables that best predicted students at risk (effective use of time and revision attempts) were deemed teachable skills that can improve with intervention

An Analysis of Preference Weights and Setting Priorities by Irrigation Advisory Services Users Based on the Analytic Hierarchy Process

Objective: Stakeholders-farmers from four different European areas (Campania (IT), Kujawsko-Pomorskie (PL), Limburg (NL), Andalusia (ES))-are asked to share, from the OPERA project, their opinions on five criteria that all aim at improving the use of irrigation advisory services (IASs). Each criterion has different characteristics that affect the way farmers rank it. The present study has two objectives. The first is to individuate the priorities of the preferences expressed by the stakeholders. The second objective is to carry out a ranking of the weights of the criteria by case study, ranking the groups and their associated properties among farmers' profiles. Methods: The answers to 120 questionnaires dispensed to the future users of IASs in the four agricultural sites were analyzed in detail, and then the given priorities were evaluated through the analytic hierarchy process (AHP). The AHP methodology was used to determine the relative weights of the five assessment criteria, and finally, to select the one with major value. Results and conclusions: The results show that A5 (assuring economic sustainability) was the most important criterion. The contributions provided by this study are twofold: Firstly, it presents an application of a methodology that involves the conversion of a linguistic judgement of farmers in a correspondence weight. Secondly, it tackles decision making regarding improving the use of IASs, evaluating the preferences expressed by the stakeholders. Irrigation advisory services can play a key role in assisting users to adopt new techniques and technologies for more efficient water use and increased production

An Epidemiology of Big Data

Federal legislation designed to transform the U.S. healthcare system and the emergence of mobile technology are among the common drivers that have contributed to a data explosion, with industry analysts and stakeholders proclaiming this decade the big data decade in healthcare (Horowitz, 2012). But a precise definition of big data is hazy (Dumbill, 2013). Instead, the healthcare industry mainly relies on metaphors, buzzwords, and slogans that fail to provide information about big data\u27s content, value, or purposes for existence (Burns, 2011). Bollier and Firestone (2010) even suggests big data does not really exist in healthcare (p. 29). While federal policymakers and other healthcare stakeholders struggle with the adoption of Meaningful Use Standards, International Classification of Diseases-10 (ICD-10), and electronic health record interoperability standards, big data in healthcare remains a widely misunderstood phenomenon. Borgman (2012) found by studying how data are created, handled, and managed in multi-disciplinary collaborations, we can inform science policy and practice (p. 12). Through the narratives of nine leaders representing three key stakeholder classes in the healthcare ecosystem: government, providers and consumers, this phenomenological research study explored a fundamental question: Within and across the narratives of three key healthcare stakeholder classes, what are the important categories of meaning or current themes about big data in healthcare? This research is significant because it: (1) produces new thematic insights about the meaning of big data in healthcare through narrative inquiry; (2) offers an agile framework of big data that can be deployed across all industries; and, (3) makes a unique contribution to scholarly qualitative literature about the phenomena of big data in healthcare for future research on topics including the diffusion and spread of health information across networks, mixed methods studies about big data, standards development, and health policy

Policy Issues Associated with Undertaking a New Large U.S. Population Cohort Study of Genes, Environment, and Disease

This report describes the efforts of the Secretary’s Advisory Committee on Genetics, Health, and Society (SACGHS) to assess the need and readiness for a new large population study (LPS) in the United States and presents recommendations to the Secretary of the U.S. Department of Health and Human Services (HHS) so that this concept can be further explored. The HHS Secretary established SACGHS in 2002 as a public forum for deliberation on the broad range of human health and societal issues raised by advances in genetics and, as warranted, the development of advice on these issues. In a March 2004 priority-setting process, SACGHS identified 11 high-priority issues warranting its attention and analysis. One of those issues was the need for an analysis of the opportunities and challenges associated with conducting an LPS aimed at understanding the relationships between genes, environments,1 and their interactions and common complex diseases. Among the considerations that led the Committee to this decision was the fact that discussions were underway at the National Institutes of Health (NIH) about whether the United States should mount a new large population-based study. In June 2005, as SACGHS factfinding efforts were beginning, NIH Director Dr. Elias A. Zerhouni requested that the Committee develop a report on the preliminary questions, steps, and strategies that would need to be addressed before considering the larger question of whether the United States should undertake a new LPS. Specifically, the Committee was asked to (1) delineate the questions that need to be addressed for policymakers to determine whether the U.S. Government should undertake a new LPS to elucidate the influences of genetic variations and environmental factors on common complex diseases; (2) explore the ways in which, or processes by which, the questions identified in step 1 can be addressed, including any intermediate research studies, pilot projects, or policy analysis efforts needed; and (3) determine the possible ways in which these questions could be addressed, taking into account the feasibility of those approaches expect the Committee to recommend solutions to the questions raised. The next section summarizes exploratory work by the National Human Genome Research Institute (NHGRI) and factfinding and consultative efforts by SACGHS on this issue. Chapter II presents the scientific basis for an LPS. Chapter III outlines the key policy issues that SACGHS has identified as warranting further attention. Chapter IV discusses the critical role that public engagement must play in determining the willingness of U.S. citizens to support and participate in such an endeavor. In keeping with its agreed upon charge, throughout this report the Committee explores the ways in which the identified policy issues could be addressed and describes possible approaches for the HHS Secretary’s consideration.http://oba.od.nih.gov/SACGHS/sacghs_focus_population.htm