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作者： Nye, C. D Kirkendall, C. D Drasgow, F Rounds, J Chernyshenko, O. S Stark, S DRASGOW CONSULTING GROUP URBANA IL ARMY RESEARCH INST FOR THE BEHAVIORAL AND SOCIAL SCIENCES FORT BELVOIR VA U.S. Army Research Institute for the Behavioral and Social Sciences Fort Belvoir VA

The Adaptive Vocational Interest Diagnostic (AVID) was developed to help recruits in the U.S. Army identify military occupational specialties (MOS) that match their interests. Initial research with the AVID demonstrated that this assessment is a valid predictor of Soldiers attitudes and performance in several MOS. The goal of the current research was to expand this previous work by examining the validity of the AVID in several new MOS and using a broader set of AVID dimensions. The data for this research were obtained from four samples of Active Duty Soldiers. In Sample 1, Soldiers were intentionally sampled from five different MOS and the validity of the AVID dimensions was compared across these occupations. In Samples 2 and 3, Soldiers from a large number of MOS were assessed and the validity of the AVID was examined in the full samples. Finally, in Sample 4, the validity of the AVID in a sample of Cyber Operators was examined. The results of these analyses indicated that the AVID has validity for predicting important military outcomes and differential validity across MOS. These findings provide additional evidence that the AVID may be useful for MOS assignment.

关键词： Applied psychology Enlisted personnel Personnel selection Psychology Regression analysis Statistics Health services Human resources descriptive analytics Mathematics Measurement Military research Physical activity Social sciences Test and evaluation Management personnel Motivation

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Effect of Prehospital Low-Titer O+ Whole Blood Administration on Vitals, Shock Index, and Delta Shock in Blunt and Penetrating Trauma Patients

Effect of Prehospital Low-Titer O+ Whole Blood Administratio...

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作者： Maksimenko, Y. M Wampler , D Everitt, B Wentling, J Dieterle, M Bullock, W Stringfellow, M Raczek, K Winckler, C. J 59th MDW San Antonio TX 59th Medical Wing San Antonio TX

Hemorrhagic shock is a leading cause of death from trauma in the prehospital setting.

关键词： Cardiovascular physiological phenomena Heart rate Hemorrhagic shock Department of defense descriptive analytics Emergency medicine Information science Data science Databases Emergencies Disasters Hospitals Statistics

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'Without data, you're just another person with an opinion'

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EXPERT REVIEW OF PHARMACOECONOMICS & OUTCOMES RESEARCH 2020年第2期20卷 147-154页

作者： Kolasa, Katarzyna Goettsch, Wim Petrova, Guenka Berler, Alexander Akad Leona Kozminskiego Hlth Econ & Healthcare Ma Dept Hlth Econ & Healthcare Management Warsaw Poland Univ Utrecht WHO Collaborating Ctr Pharmaceut Policy Div Pharmacoepidemiol & Clin Pharmacol Utrecht Netherlands Med Univ Sofia Fac Pharm Dept Social Pharm & Pharmacoecon Sofia Bulgaria Gnomon Dept Hlth Econ & Healthcare Management Athens Greece

Introduction: Given the recent impressive digital transformation worldwide, the importance of data has reached a new dimension. It is, therefore, provocative to ask whether data can save healthcare systems from bankruptcy. Areas covered: We reviewed published examples in the search for the evidence on how the growing amount of data could change the way we used to assess the value of healthcare technologies, ensuring a more holistic approach in the decision-making process while reducing the waste in the healthcare. Expert opinion: The growing amount of data will continue to provide a multitude of valuable insights that can save healthcare systems from bankruptcy. Electronic medical records, IoT, wearables, and mobile applications generate constant data streams that can be utilized endlessly thanks to methodological advancements such as SNA, unsupervised and supervised machine learning, and natural language programming. However, interoperability across these multiple data sources still pose a challenge for the future development of data-driven healthcare. Already today however, decision makers can utilize Big Data to develop conditional coverage schemes for very expensive and complicated health technologies suitable for personalized healthcare. More advanced payers may utilize even data analytics even further and develop AI-based pricing schemes.

关键词： Big data predictive modeling descriptive analytics prescriptive analytics predictive analytics

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Current Trends in the Use of Guided Technology in Surgical Endodontics: A Web-based Survey.

Current Trends in the Use of Guided Technology in Surgical E...

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作者： Reimnitz, L. M.

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analytics Everywhere: Generating Insights From the Internet of Things

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IEEE ACCESS 2019年 7卷 71749-71769页

作者： Cao, Hung Wachowicz, Monica Renso, Chiara Carlini, Emanuele Univ New Brunswick People Mot Lab Fredericton NB E3B 5A3 Canada CNR ISTI HPC Lab I-56127 Pisa Italy

The Internet of Things is expected to generate an unprecedented number of unbounded data streams that will produce a paradigm shift when it comes to data analytics. We are moving away from performing analytics in a public or private cloud to performing analytics locally at the fog and edge resources. In this paper, we propose a network of tasks utilizing edge, fog, and cloud computing that are designed to support an analytics Everywhere framework. The aim is to integrate a variety of computational resources and analytical capabilities according to a data life-cycle. We demonstrate the proposed framework using an application in smart transit.

关键词： descriptive analytics diagnostic analytics predictive analytics edge computing fog computing cloud computing Internet of Things

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The determinants of health assessment in the United States: A supervised learning approach

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Healthcare analytics 2022年 2卷 100106-100106页

作者： Coqueret, Guillaume EMLYON Business School 23 avenue Guy de Collongue Ecully 69130 France

In this article, we exploit a large dataset of surveys to answer a simple questions: which factors drive good (or bad) health? Using a set of 14 very diverse predictors (both socioeconomic and physiological), we perform sets of supervised learning tasks to determine which variables best explain the self-assessment of health conditions. Our predictive algorithms range from simple regressions to tabular networks and include random forests, all of which allow for some interpretability, directly or indirectly, either via feature importance or via conditional permutation influence of the trained models. Our results indicate that two indicators, in particular, emerge as potent determinants of physical well-being: income and exercise. The body mass index is the third main driver, though its role is less prominent. Importantly, for reproducibility, the dataset used in the study is in open access. © 2022 The Author(s)

关键词： Data mining descriptive analytics Feature importance Permutation importance Supervised learning Tabular networks

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Prevalence of HCV in Shopian: Using Data Visualization (Data Mining) 3rd

Prevalence of HCV in Shopian: Using Data Visualization (Data...

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3rd International Conference on Intelligent Computing and Communication (ICICC)

作者： Ahmad, Zubair Seethlani, Jitendra Gul, Sheikh Babar Sri Satya Sai Univ Technol & Med Sci Dept Comp Sci Sehore 466001 MP India Mini Secretariat Chief Med Off Block B Shopian 192303 J&K India

ISBN: (纸本)9789811510847;9789811510830

Data mining is an important subfield of Data Science and is also called knowledge discovery within a database. In this paper, we actually use data mining visualization techniques to analyze the huge database of District Shopian and assume prevalence rate of HCV in Specific Area (Vehil) of District Shopian (J&K). Health Department of District Shopian uses different types of registers to maintain the data of patients. During survey, we go through the registers to collect data of symptomatic HCV patients analyze it by maintaining the disease trends. Data mining analytical tools were being used to detect prevalence of HCV infection distribution and also identify the reasons that contributed to such widespread of HCV in the area of Vehil

关键词： descriptive analytics Predictive analytics Data visualization techniques hepatic C CASE STUDY

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Smart Cities for Sustainability An Analytical Perspective

Smart Cities for Sustainability An Analytical Perspective

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4th World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4)

作者： Mundada, Monica Mukkamala, Raghava Rao Copenhagen Business Sch Ctr Business Data Analyt Dept Digitalizat Copenhagen Denmark Kristiania Univ Coll Dept Technol Oslo Norway

ISBN: (纸本)9781728168234

Smart cities is an trending concept with an focus of development in urban areas from all edges. The primary aim of this research work, is concentrated for literature review on Smart Economy, Smart Environment, Smart Government, Smart Living, Smart Mobility, Smart People integrated with technology research themes with a key focus on data analytics approach towards sustainable development. Our conclusion reveals that a lot of research in smart cities is focused on prescriptive analytics, which indicates that smart cities initiatives aligned on taking the data analytics perspective for sustainable development in urban areas.

关键词： Smart cities Sustainable Development Predictive analytics descriptive analytics Prescriptive analytics

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Open Data Visual analytics to Support Decisions on Physical Investments 5

Open Data Visual Analytics to Support Decisions on Physical ...

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5th Information Systems International Conference (ISICO) on Role of Information Systems in Industry 4.0

作者： Wasesa, Meditya Mashuri, M. Handayani, Putri Putro, Utomo S. Bandung Inst Technol Sch Business & Management Jl Ganesha 10 Bandung Indonesia Indonesian Ctr Logist & Value Chains Jl Sarijadi Baru III 15 Bandung Indonesia Univ Utrecht Water Sci & Management Masters Programme Utrecht Netherlands

Before making decisions about the location of physical investments (e.g. factories, warehouses, stores, etc.), investors have to conduct thorough profiling on the prospective locations of the physical facilities. For this profiling purpose, investors consider a number of important aspects such as the location's local resources, market size, economic growth, regional minimum wage (salary) standard, land acquisition cost, human development index, and gross domestic product (GDP). While conducting manual research on those decision variables can cost extensive time and efforts, in this article, we present an open data visual analytics which will help investors in making decisions on the prospective location of physical investments. Investors can use the web-based application to easily gather information and do a quick comparison among prospective investment locations in terms of the selected decision variables. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Open Data Visual analytics descriptive analytics Spatiotemporal Data Physical Investment

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Beyond boundaries: Charting the frontier of healthcare with big data and ai advancements in pharmacovigilance

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Health Sciences Review 2025年 14卷 100214-100214页

作者： Agarwal, Arohi Singh, Gagan Jain, Samyak Mittal, Piyush Department of pharmacy practice Teerthanker Mahaveer college of pharmacy b-3 green valley aam bagh Rishikesh Uttarakhand 249203 India Vice-principal and Professor Teerthanker Mahaveer college of pharmacy India

AbstractThe healthcare sector is intricate, generating vast amounts of data from various sources at an accelerated pace. The contemporary trend of Big Data analytics is pivotal, impacting not only the pharmaceutical industry but also transforming healthcare, contributing to personalized treatment, aiding in preventive healthcare, managing electronic health records, facilitating adverse drug reporting, and incorporating consumer reviews. This article provides an overview of the inevitable influence of big data and the utilization of artificial intelligence in revolutionizing both healthcare and the pharmaceutical sector. It delves into the notable benefits and challenges encountered in advancing data analytics of the early 21st *** many countries, Post-marketing surveillance of drug safety relinquishes on a systematic analysis of spontaneous using Generative artificial intelligence (AI) to overcome gaps in the present PV ecosystem is critical to maintaining an uninterrupted record of security and effectiveness within healthcare analytics, data mining techniques, predictive analytics, and the emergence of scientific fields like bioinformatics and health informatics are empowered by Big Data. Nevertheless, the integration of AI in healthcare, especially in pharmacovigilance, aligns with the evolving landscape of electronic health information technology. In conclusion, review highlights the transformative impact of Big Data and AI in healthcare, emphasizing their applications in pharmacovigilance and pharmacoepidemiology. The continuous evolution of these technologies holds promise for improving patient safety, personalized medicine, and overall healthcare outcomes.

关键词： Big data Big data analytics Genomics Structured data Unstructured data Semi-structured data descriptive analytics Exploratory or discovery analytics Predictive analytics Pharmacovigilance (Pv) Artificial intelligence (AI) Generative adversarial networks Variational autoencoders Transformer-based models

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