scholarly journals Data Empowerment of Decision-Makers in an Era of a Pandemic: Intersection of “Classic” and Artificial Intelligence in the Service of Medicine (Preprint)

2020 ◽  
Author(s):  
Gil A Geva ◽  
Itay Ketko ◽  
Maya Nitecki ◽  
Shoham Simon ◽  
Barr Inbar ◽  
...  
Keyword(s):  
Data Science ◽  
Severe Disease ◽  
True Positive Rate ◽  
Decision Makers ◽  
Multidisciplinary Teams ◽  
Individual Level ◽  
Key Factor ◽  
High True Positive Rate ◽  
Rich Data ◽  
Prompt Action

BACKGROUND The COVID-19 outbreak required prompt action by health authorities around the world in response to a novel threat. With enormous amounts of information originating in sources with uncertain degree of validation and accuracy, it is essential to provide executive-level decision-makers with the most actionable, pertinent, and updated data analysis to enable them to adapt their strategy swiftly and competently. OBJECTIVE We report here the origination of a COVID-19 dedicated response in the Israel Defense Forces with the assembly of an operational Data Center for the Campaign against Coronavirus. METHODS Spearheaded by directors with clinical, operational, and data analytics orientation, a multidisciplinary team utilized existing and newly developed platforms to collect and analyze large amounts of information on an individual level in the context of SARS-CoV-2 contraction and infection. RESULTS Nearly 300,000 responses to daily questionnaires were recorded and were merged with other data sets to form a unified data lake. By using basic as well as advanced analytic tools ranging from simple aggregation and display of trends to data science application, we provided commanders and clinicians with access to trusted, accurate, and personalized information and tools that were designed to foster operational changes and mitigate the propagation of the pandemic. The developed tools aided in the in the identification of high-risk individuals for severe disease and resulted in a 30% decline in their attendance to their units. Moreover, the queue for laboratory examination for COVID-19 was optimized using a predictive model and resulted in a high true-positive rate of 20%, which is more than twice as high as the baseline rate (2.28%, 95% CI 1.63%-3.19%). CONCLUSIONS In times of ambiguity and uncertainty, along with an unprecedented flux of information, health organizations may find multidisciplinary teams working to provide intelligence from diverse and rich data a key factor in providing executives relevant and actionable support for decision-making.

What is the Business with AI? Preparing Future Decision Makers and Leaders

2020 ◽  
Author(s):  
Helena S. Wisniewski
Keyword(s):  
Data Science ◽  
Decision Makers ◽  
Management Skills ◽  
Business Courses ◽  
College Of Business ◽  
College Of Engineering ◽  
Investment Firm ◽  
Business Curricula ◽  
The University ◽  
The Impact

With companies now recognizing how artificial intelligence (AI), digitalization, the internet of things (IoT), and data science affect value creation and the maintenance of a competitive advantage, their demand for talented individuals with both management skills and a strong understanding of technology will grow dramatically. There is a need to prepare and train our current and future decision makers and leaders to have an understanding of AI and data science, the significant impact these technologies are having on business, how to develop AI strategies, and the impact all of this will have on their employees’ roles. This paper discusses how business schools can fulfill this need by incorporating AI into their business curricula, not only as stand-alone courses but also integrated into traditional business sequences, and establishing interdisciplinary efforts and collaborative industry partnerships. This article describes how the College of Business and Public Policy (CBPP) at the University of Alaska Anchorage is implementing multiple approaches to meet these needs and prepare future leaders and decision makers. These approaches include a detailed description of CBPP’s first AI course and related student successes, the integration of AI into additional business courses such as entrepreneurship and GSCM, and the creation of an AI and Data Science Lab in partnership with the College of Engineering and an investment firm.

scholarly journals No neighbour-induced increase in root growth of soybean and sunflower in mesh divider experiments after controlling for nutrient concentration and soil volume

AoB Plants ◽  
2021 ◽  
Author(s):  
Bin J W Chen ◽  
Li Huang ◽  
Heinjo J During ◽  
Xinyu Wang ◽  
Jiahe Wei ◽  
...  
Keyword(s):  
Root Growth ◽  
Nutrient Concentration ◽  
Ideal Free Distribution ◽  
Plant Performance ◽  
Root Competition ◽  
Individual Level ◽  
Key Factor ◽  
Promising Solution ◽  
Soil Volume ◽  
Ideal Free

Abstract Root competition is a key factor determining plant performance, community structure and ecosystem productivity. To adequately estimate the extent of root proliferation of plants in response to neighbours independently of nutrient availability, one should use a setup that can simultaneously control for both nutrient concentration and soil volume at plant individual level. With a mesh-divider design, which was suggested as a promising solution for this problem, we conducted two intraspecific root competition experiments one with soybean (Glycine max) and the other with sunflower (Helianthus annuus). We found no response of root growth or biomass allocation to intraspecific neighbours, i.e. an ‘ideal free distribution’ (IDF) norm, in soybean; and even a reduced growth as a negative response in sunflower. These responses are all inconsistent with the hypothesis that plants should produce more roots even at the expense of reduced fitness in response to neighbours, i.e. root over-proliferation. Our results suggest that neighbour-induced root over-proliferation is not a ubiquitous feature in plants. By integrating the findings with results from other soybean studies, we conclude that for some species this response could be a genotype-dependent response as a result of natural or artificial selection, or a context-dependent response so that plants can switch from root over-proliferation to IDF depending on the environment of competition. We also critically discuss whether the mesh-driver design is the ideal solution for root competition experiments.

scholarly journals Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Records

2016 ◽  
Vol 24 (2) ◽  
pp. 263-272 ◽  
Author(s):  
Kosuke Imai ◽  
Kabir Khanna
Keyword(s):  
Mean Squared Error ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Voter Registration ◽  
Racial Groups ◽  
Ecological Inference ◽  
Inference Problem ◽  
Individual Level ◽  
Positive Rate ◽  
Election Results

In both political behavior research and voting rights litigation, turnout and vote choice for different racial groups are often inferred using aggregate election results and racial composition. Over the past several decades, many statistical methods have been proposed to address this ecological inference problem. We propose an alternative method to reduce aggregation bias by predicting individual-level ethnicity from voter registration records. Building on the existing methodological literature, we use Bayes's rule to combine the Census Bureau's Surname List with various information from geocoded voter registration records. We evaluate the performance of the proposed methodology using approximately nine million voter registration records from Florida, where self-reported ethnicity is available. We find that it is possible to reduce the false positive rate among Black and Latino voters to 6% and 3%, respectively, while maintaining the true positive rate above 80%. Moreover, we use our predictions to estimate turnout by race and find that our estimates yields substantially less amounts of bias and root mean squared error than standard ecological inference estimates. We provide open-source software to implement the proposed methodology.

scholarly journals Assessment of changes in efficacy of antimalarials: a systematic review and network meta-regression

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
S W Youdom ◽  
R S Tchouenkou ◽  
E-P Ndong-Nguema ◽  
L K Basco
Keyword(s):  
Systematic Review ◽  
Uncomplicated Malaria ◽  
Data Science ◽  
Meta Analysis ◽  
Decision Makers ◽  
Inclusion Criteria ◽  
Antimalarial Drug Resistance ◽  
Consistency Model ◽  
Meta Regression ◽  
Methodological Studies

Abstract Background The fight against diseases such as malaria requires the synthesis of evidence from existing studies to inform decision makers. Indeed, at a cross road of antimalarial drug resistance, several artemisinin-based combination therapies (ACT) with multiple doses are available to fight uncomplicated malaria. However, little is known on how these combinations are combined as well as how different formulations are tested. Methods A systematic review was performed to identify randomized trials. Articles were sought by hand-searching and scanning references. Additional covariates effect on treatment outcome was assessed, and a modeling approach to reduce heterogeneity among trials was evaluated. We explored one single interaction effect for all treatment with age as the main covariate in a meta-regression. A Bayesian analysis was used to implement the consistency and inconsistency models under the WinBUGS software. Ranking measure was used to obtain a hierarchy of the competing interventions. Results In total, 77 articles meet the inclusion criteria with 15 combinations tested in 36,000 patients. Results were compared to that of frequentist approach and presented according to the Prisma NMA checklist. The consistency model showed a good performance than the inconsistency model under the hypothesis of homogeneity. It was found that compared to artemether-lumefantrine, the dihydro-artemisinin-piperaquine was more effective before (B, OR = 1.83; 95% CI = 1.31-2.56) and after (A, OR = 1.70; 95% CI = 1.20-2.43) covariate adjustment, and occupied the top rank. Conclusions The application of the methods described here may be helpful to gain better understanding of treatment efficacy and improve future decisions in malaria programs. Based on the available evidence, this study demonstrated the superiority of DHAP among currently recommended ACT in preventing as well as treating uncomplicated malaria. Key messages Choosing the best therapy requires data triangulation and data science. Network meta-analysis could be a solution but need more methodological studies.

scholarly journals A Trip Purpose-Based Data-Driven Alighting Station Choice Model Using Transit Smart Card Data

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Kai Lu ◽  
Alireza Khani ◽  
Baoming Han
Keyword(s):  
Open Systems ◽  
Choice Model ◽  
Mixed Logit ◽  
Random Coefficients ◽  
Logit Models ◽  
Transit Systems ◽  
Trip Purpose ◽  
Mixed Logit Models ◽  
Key Factor ◽  
Rich Data

Automatic fare collection (AFC) systems have been widely used all around the world which record rich data resources for researchers mining the passenger behavior and operation estimation. However, most transit systems are open systems for which only boarding information is recorded but the alighting information is missing. Because of the lack of trip information, validation of utility functions for passenger choices is difficult. To fill the research gaps, this study uses the AFC data from Beijing metro, which is a closed system and records both boarding information and alighting information. To estimate a more reasonable utility function for choice modeling, the study uses the trip chaining method to infer the actual destination of the trip. Based on the land use and passenger flow pattern, applying k-means clustering method, stations are classified into 7 categories. A trip purpose labelling process was proposed considering the station category, trip time, trip sequence, and alighting station frequency during five weekdays. We apply multinomial logit models as well as mixed logit models with independent and correlated normally distributed random coefficients to infer passengers’ preferences for ticket fare, walking time, and in-vehicle time towards their alighting station choice based on different trip purposes. The results find that time is a combined key factor while the ticket price based on distance is not significant. The estimated alighting stations are validated with real choices from a separate sample to illustrate the accuracy of the station choice models.

scholarly journals Interventions to Promote a Healthy Sexuality among School Adolescents: A Scoping Review

2021 ◽  
Vol 11 (11) ◽  
pp. 1155
Author(s):  
Fernanda Loureiro ◽  
Margarida Ferreira ◽  
Paula Sarreira-de-Oliveira ◽  
Vanessa Antunes
Keyword(s):  
Open Science ◽  
Multidisciplinary Teams ◽  
Adolescent Sexual Behavior ◽  
Scoping Reviews ◽  
Key Factor ◽  
Nurses Role ◽  
Healthy Sexuality ◽  
Science Framework ◽  
Meta Analyses

Schools are particularly suitable contexts for the implementation of interventions focused on adolescent sexual behavior. Sexual education and promotion have a multidisciplinary nature. Nurses’ role and the spectrum of the carried-out interventions is not clear. We aimed to identify interventions that promote a healthy sexuality among school adolescents. Our review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews and was registered in the Open Science Framework. Published articles on sexuality in adolescents in school contexts were considered. The research limitations included primary studies; access in full text in English, Spanish, or Portuguese; and no data publication limitation. Research was carried out on the EBSCOhost, PubMed, SciELO, and Web of Science platforms; gray literature and the bibliographies of selected articles were also searched. A total of 56 studies were included in the sample. The studies used a broad range of research methods, and 10 types of interventions were identified. Multi-interventional programs and socio-emotional interventions showed a greater impact on long-term behavioral changes, and continuity seemed to be a key factor. Long-term studies are needed to reach a consensus on the effectiveness of interventions. Nurses’ particular role on the multidisciplinary teams was found to be a gap in the research, and must be further explored.

scholarly journals Designing Efficient Material Handling Systems Via Automated Guided Vehicles (AGVs)

2018 ◽  
Vol 5 (2) ◽  
pp. 97 ◽  
Author(s):  
Carlos Llopis-Albert ◽  
Francisco Rubio ◽  
Francisco Valero
Keyword(s):  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Material Handling ◽  
Qualitative Comparative Analysis ◽  
Decision Makers ◽  
Automated Guided Vehicles ◽  
Managerial Decision ◽  
Material Handling Systems ◽  
Level Of Satisfaction ◽  
Key Factor

<p class="Textoindependiente21">The designing of an efficient warehouse management system is a key factor to improve productivity and reduce costs. The use of Automated Guided Vehicles (AVGs) in Material Handling Systems (MHS) and Flexible Manufacturing Systems (FMS) can help to that purpose. This paper is intended to provide insight regarding the technical and financial suitability of the implementation of a fleet of AGVs. This is carried out by means of a fuzzy set/qualitative comparative analysis (fsQCA) by measuring the level of satisfaction of managerial decision makers.</p>

scholarly journals Collaboration between Government and Research Community to Respond to COVID-19: Israel’s Case

2021 ◽  
Vol 7 (4) ◽  
pp. 208
Author(s):  
Mor Peleg ◽  
Amnon Reichman ◽  
Sivan Shachar ◽  
Tamir Gadot ◽  
Meytal Avgil Tsadok ◽  
...  
Keyword(s):  
Health Policy ◽  
Data Science ◽  
Research Community ◽  
Remote Access ◽  
Data Driven ◽  
Multidisciplinary Teams ◽  
Science Methods ◽  
Research Environments ◽  
Future Data ◽  
The Government

Triggered by the COVID-19 crisis, Israel’s Ministry of Health (MoH) held a virtual datathon based on deidentified governmental data. Organized by a multidisciplinary committee, Israel’s research community was invited to offer insights to help solve COVID-19 policy challenges. The Datathon was designed to develop operationalizable data-driven models to address COVID-19 health policy challenges. Specific relevant challenges were defined and diverse, reliable, up-to-date, deidentified governmental datasets were extracted and tested. Secure remote-access research environments were established. Registration was open to all citizens. Around a third of the applicants were accepted, and they were teamed to balance areas of expertise and represent all sectors of the community. Anonymous surveys for participants and mentors were distributed to assess usefulness and points for improvement and retention for future datathons. The Datathon included 18 multidisciplinary teams, mentored by 20 data scientists, 6 epidemiologists, 5 presentation mentors, and 12 judges. The insights developed by the three winning teams are currently considered by the MoH as potential data science methods relevant for national policies. Based on participants’ feedback, the process for future data-driven regulatory responses for health crises was improved. Participants expressed increased trust in the MoH and readiness to work with the government on these or future projects.

scholarly journals PF : Website Fingerprinting Attack Using Probabilistic Topic Model

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Hongcheng Zou ◽  
Ziling Wei ◽  
Jinshu Su ◽  
Baokang Zhao ◽  
Yusheng Xia ◽  
...  
Keyword(s):  
Topic Model ◽  
False Positive Rate ◽  
True Positive Rate ◽  
The Other ◽  
Open World ◽  
Closed World ◽  
Probabilistic Topic Model ◽  
High True Positive Rate ◽  
Positive Rate ◽  
Fingerprinting Attack

Website fingerprinting (WFP) attack enables identifying the websites a user is browsing even under the protection of privacy-enhancing technologies (PETs). Previous studies demonstrate that most machine-learning attacks need multiple types of features as input, thus inducing tremendous feature engineering work. However, we show the other alternative. That is, we present Probabilistic Fingerprinting (PF), a new website fingerprinting attack that merely leverages one type of features. They are produced by using a mathematical model PWFP that combines a probabilistic topic model with WFP for the first time, due to a finding that a plain text and the sequence file generated from a traffic instance are essentially the same. Experimental results show that the proposed new features are more distinguishing than the existing features. In a closed-world setting, PF attains a better accuracy performance (99.79% at most) than prior attacks on various datasets gathered in the scenarios of Shadowsocks, SSH, and TLS, respectively. Besides, even when the number of training instances drops to as few as 4, PF still reaches an accuracy of above 90%. In the more realistic open-world setting, PF attains a high true positive rate (TPR) and Bayes detection rate (BDR), and a low false positive rate (FPR) in all evaluations, which outperforms the other attacks. These results highlight that it is meaningful and possible to explore new features to improve the accuracy of WFP attacks.

Leveraging data science to enhance suicide prevention research: a literature review

2021 ◽  
pp. injuryprev-2021-044322
Author(s):  
Avital Rachelle Wulz ◽  
Royal Law ◽  
Jing Wang ◽  
Amy Funk Wolkin
Keyword(s):  
Risk Factors ◽  
Literature Review ◽  
Injury Prevention ◽  
Suicide Prevention ◽  
Data Science ◽  
Population Level ◽  
Individual Risk ◽  
Prevention Research ◽  
Science Method ◽  
Individual Level

ObjectiveThe purpose of this research is to identify how data science is applied in suicide prevention literature, describe the current landscape of this literature and highlight areas where data science may be useful for future injury prevention research.DesignWe conducted a literature review of injury prevention and data science in April 2020 and January 2021 in three databases.MethodsFor the included 99 articles, we extracted the following: (1) author(s) and year; (2) title; (3) study approach (4) reason for applying data science method; (5) data science method type; (6) study description; (7) data source and (8) focus on a disproportionately affected population.ResultsResults showed the literature on data science and suicide more than doubled from 2019 to 2020, with articles with individual-level approaches more prevalent than population-level approaches. Most population-level articles applied data science methods to describe (n=10) outcomes, while most individual-level articles identified risk factors (n=27). Machine learning was the most common data science method applied in the studies (n=48). A wide array of data sources was used for suicide research, with most articles (n=45) using social media and web-based behaviour data. Eleven studies demonstrated the value of applying data science to suicide prevention literature for disproportionately affected groups.ConclusionData science techniques proved to be effective tools in describing suicidal thoughts or behaviour, identifying individual risk factors and predicting outcomes. Future research should focus on identifying how data science can be applied in other injury-related topics.

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