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1. AI in Health: State of the Art, Challenges, and Future Directions

   https://doi.org/10.1055/s-0039-1677908  

   Wang, Fei ; Preininger, Anita ( August 2019 , Yearbook of Medical Informatics)

   Introduction: Artificial intelligence (AI) technologies continue to attract interest from a broad range of disciplines in recent years, including health. The increase in computer hardware and software applications in medicine, as well as digitization of health-related data together fuel progress in the development and use of AI in medicine. This progress provides new opportunities and challenges, as well as directions for the future of AI in health. Objective: The goals of this survey are to review the current state of AI in health, along with opportunities, challenges, and practical implications. This review highlights recent developments over the past five years and directions for the future. Methods: Publications over the past five years reporting the use of AI in health in clinical and biomedical informatics journals, as well as computer science conferences, were selected according to Google Scholar citations. Publications were then categorized into five different classes, according to the type of data analyzed. Results: The major data types identified were multi-omics, clinical, behavioral, environmental and pharmaceutical research and development (R&D) data. The current state of AI related to each data type is described, followed by associated challenges and practical implications that have emerged over the last several years. Opportunities and future directions based on these advances are discussed. Conclusion: Technologies have enabled the development of AI-assisted approaches to healthcare. However, there remain challenges. Work is currently underway to address multi-modal data integration, balancing quantitative algorithm performance and qualitative model interpretability, protection of model security, federated learning, and model bias. 

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2. Climate change and infectious disease: a review of evidence and research trends

   https://doi.org/10.1186/s40249-023-01102-2  

   Van de Vuurst, Paige ; Escobar, Luis E. ( May 2023 , Infectious Diseases of Poverty)

   Climate change presents an imminent threat to almost all biological systems across the globe. In recent years there have been a series of studies showing how changes in climate can impact infectious disease transmission. Many of these publications focus on simulations based on in silico data, shadowing empirical research based on field and laboratory data. A synthesis work of empirical climate change and infectious disease research is still lacking.

   We conducted a systemic review of research from 2015 to 2020 period on climate change and infectious diseases to identify major trends and current gaps of research. Literature was sourced from Web of Science and PubMed literary repositories using a key word search, and was reviewed using a delineated inclusion criteria by a team of reviewers.

   Our review revealed that both taxonomic and geographic biases are present in climate and infectious disease research, specifically with regard to types of disease transmission and localities studied. Empirical investigations on vector-borne diseases associated with mosquitoes comprised the majority of research on the climate change and infectious disease literature. Furthermore, demographic trends in the institutions and individuals published revealed research bias towards research conducted across temperate, high-income countries. We also identified key trends in funding sources for most resent literature and a discrepancy in the gender identities of publishing authors which may reflect current systemic inequities in the scientific field.

   Future research lines on climate change and infectious diseases should considered diseases of direct transmission (non-vector-borne) and more research effort in the tropics. Inclusion of local research in low- and middle-income countries was generally neglected. Research on climate change and infectious disease has failed to be socially inclusive, geographically balanced, and broad in terms of the disease systems studied, limiting our capacities to better understand the actual effects of climate change on health.

    

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3. Journal Aims Scope and Further Details

   https://doi.org/10.7910/DVN/ASMDSA  

   Kaiser, Kendra ; Braswell, Anna ; Fork, Megan ; Fork, Megan ( January 2022 , Harvard Dataverse)

   Detailed information and published mission or aims scope for journals in which 3 or more publications from the dataset Publications associated with SES grants, 2000-2015 appeared. CSV file with 10 columns and names in header row: journal is the name of the scientific journal or outlet in which at least 3 papers were published (text); number of papers is the number of papers from the dataset Publications associated with SES grants, 2000-2015 published in the journal (integer); Impact factor is the most recent available Impact Factor for the journal as of March 2020 (float); Discipline is the broad disciplinary category to which the journal belongs, as identified by the authors of this dataset (text); Stated aimsscope is the text of the journal aimsscope as provided on the journal website (text); Mission includes interdisciplinary? categorizes whether the stated aimsscope of the journal includes dissemination of interdisciplinary research (Y indicates the stated aimsscope explicitly include interdisciplinary research, I indicates that publication of interdisciplinary research is implicit but not directly stated in the aimsscope, N indicates there is no evidence that interdisciplinary research are part of the aimsscope of the journal); Mission includes humans/social? categorizes whether the stated aimsscope of the journal includes dissemination of research about human or social systems (Y indicates the stated aimsscope include some mention of human impacts, social systems, etc., N indicates there is no evidence that research on human or social systems are part of the aimsscope of the journal) Gutcheck Interdisciplinary? is an evaluation of whether the journal publishes interdisciplinary research as a matter of course, as judged by the authors of the dataset (Y indicates the journal publishes interdisciplinary research s a matter of course, N indicates journal does not tend to publish interdisciplinary research, kinda to indicate some history of publishing interdisciplinary research that may not be a major focus of published content. Forward slashes between values show where the dataset authors differed in their assessments.); Gutcheck CNHS? is an evaluation of whether the journal publishes research on socio-environmental systems (social-ecological systems, coupled natural and human systems) as a matter of course, as judged by the authors of the dataset (Y indicates the journal publishes research on socio-environmental systems as a matter of course, N indicates journal does not tend to publish research on socio-environmental systems , kinda to indicate some history of publishing research on socio-environmental systems that may not be a major focus of published content. Forward slashes between values show where the dataset authors differed in their assessments.); Notes provide any other notes added by the authors of this dataset during our processing of these data (text). 

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4. A Multi-match Approach to the Author Uncertainty Problem

   https://doi.org/10.2478/jdis-2019-0006  

   Carley, Stephen F. ; Porter, Alan L. ; Youtie, Jan L. ( June 2019 , Journal of Data and Information Science)

   Abstract Purpose The ability to identify the scholarship of individual authors is essential for performance evaluation. A number of factors hinder this endeavor. Common and similarly spelled surnames make it difficult to isolate the scholarship of individual authors indexed on large databases. Variations in name spelling of individual scholars further complicates matters. Common family names in scientific powerhouses like China make it problematic to distinguish between authors possessing ubiquitous and/or anglicized surnames (as well as the same or similar first names). The assignment of unique author identifiers provides a major step toward resolving these difficulties. We maintain, however, that in and of themselves, author identifiers are not sufficient to fully address the author uncertainty problem. In this study we build on the author identifier approach by considering commonalities in fielded data between authors containing the same surname and first initial of their first name. We illustrate our approach using three case studies. Design/methodology/approach The approach we advance in this study is based on commonalities among fielded data in search results. We cast a broad initial net—i.e., a Web of Science (WOS) search for a given author’s last name, followed by a comma, followed by the first initial of his or her first name (e.g., a search for ‘John Doe’ would assume the form: ‘Doe, J’). Results for this search typically contain all of the scholarship legitimately belonging to this author in the given database (i.e., all of his or her true positives), along with a large amount of noise, or scholarship not belonging to this author (i.e., a large number of false positives). From this corpus we proceed to iteratively weed out false positives and retain true positives. Author identifiers provide a good starting point—e.g., if ‘Doe, J’ and ‘Doe, John’ share the same author identifier, this would be sufficient for us to conclude these are one and the same individual. We find email addresses similarly adequate—e.g., if two author names which share the same surname and same first initial have an email address in common, we conclude these authors are the same person. Author identifier and email address data is not always available, however. When this occurs, other fields are used to address the author uncertainty problem. Commonalities among author data other than unique identifiers and email addresses is less conclusive for name consolidation purposes. For example, if ‘Doe, John’ and ‘Doe, J’ have an affiliation in common, do we conclude that these names belong the same person? They may or may not; affiliations have employed two or more faculty members sharing the same last and first initial. Similarly, it’s conceivable that two individuals with the same last name and first initial publish in the same journal, publish with the same co-authors, and/or cite the same references. Should we then ignore commonalities among these fields and conclude they’re too imprecise for name consolidation purposes? It is our position that such commonalities are indeed valuable for addressing the author uncertainty problem, but more so when used in combination. Our approach makes use of automation as well as manual inspection, relying initially on author identifiers, then commonalities among fielded data other than author identifiers, and finally manual verification. To achieve name consolidation independent of author identifier matches, we have developed a procedure that is used with bibliometric software called VantagePoint (see www.thevantagepoint.com) While the application of our technique does not exclusively depend on VantagePoint, it is the software we find most efficient in this study. The script we developed to implement this procedure is designed to implement our name disambiguation procedure in a way that significantly reduces manual effort on the user’s part. Those who seek to replicate our procedure independent of VantagePoint can do so by manually following the method we outline, but we note that the manual application of our procedure takes a significant amount of time and effort, especially when working with larger datasets. Our script begins by prompting the user for a surname and a first initial (for any author of interest). It then prompts the user to select a WOS field on which to consolidate author names. After this the user is prompted to point to the name of the authors field, and finally asked to identify a specific author name (referred to by the script as the primary author) within this field whom the user knows to be a true positive (a suggested approach is to point to an author name associated with one of the records that has the author’s ORCID iD or email address attached to it). The script proceeds to identify and combine all author names sharing the primary author’s surname and first initial of his or her first name who share commonalities in the WOS field on which the user was prompted to consolidate author names. This typically results in significant reduction in the initial dataset size. After the procedure completes the user is usually left with a much smaller (and more manageable) dataset to manually inspect (and/or apply additional name disambiguation techniques to). Research limitations Match field coverage can be an issue. When field coverage is paltry dataset reduction is not as significant, which results in more manual inspection on the user’s part. Our procedure doesn’t lend itself to scholars who have had a legal family name change (after marriage, for example). Moreover, the technique we advance is (sometimes, but not always) likely to have a difficult time dealing with scholars who have changed careers or fields dramatically, as well as scholars whose work is highly interdisciplinary. Practical implications The procedure we advance has the ability to save a significant amount of time and effort for individuals engaged in name disambiguation research, especially when the name under consideration is a more common family name. It is more effective when match field coverage is high and a number of match fields exist. Originality/value Once again, the procedure we advance has the ability to save a significant amount of time and effort for individuals engaged in name disambiguation research. It combines preexisting with more recent approaches, harnessing the benefits of both. Findings Our study applies the name disambiguation procedure we advance to three case studies. Ideal match fields are not the same for each of our case studies. We find that match field effectiveness is in large part a function of field coverage. Comparing original dataset size, the timeframe analyzed for each case study is not the same, nor are the subject areas in which they publish. Our procedure is more effective when applied to our third case study, both in terms of list reduction and 100% retention of true positives. We attribute this to excellent match field coverage, and especially in more specific match fields, as well as having a more modest/manageable number of publications. While machine learning is considered authoritative by many, we do not see it as practical or replicable. The procedure advanced herein is both practical, replicable and relatively user friendly. It might be categorized into a space between ORCID and machine learning. Machine learning approaches typically look for commonalities among citation data, which is not always available, structured or easy to work with. The procedure we advance is intended to be applied across numerous fields in a dataset of interest (e.g. emails, coauthors, affiliations, etc.), resulting in multiple rounds of reduction. Results indicate that effective match fields include author identifiers, emails, source titles, co-authors and ISSNs. While the script we present is not likely to result in a dataset consisting solely of true positives (at least for more common surnames), it does significantly reduce manual effort on the user’s part. Dataset reduction (after our procedure is applied) is in large part a function of (a) field availability and (b) field coverage. 

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5. National Symposium on PRedicting Emergence of Virulent Entities by Novel Technologies (PREVENT)". NSF Predictive Intelligence for Pandemic Prevention (PIPP) Workshop Summary Report. Atlanta, GA: Georgia Tech. 52 pages.

   Prakash, B.A. ; Torrens, P.M. ; Wigginton, K. ; Yin, J. ( January 2021 , National Symposium on PRedicting Emergence of Virulent Entities by Novel Technologies (PREVENT))

   null (Ed.)

   The National Science Foundation (NSF) held a virtual Symposium on PRedicting Emergence of Virulent Entities by Novel Technologies (PREVENT), on February 22 – 23, 2021 as part of its series on Predictive Intelligence for Pandemic Prevention (PIPP). The workshop brought together more than 60 leading experts, representing NSF research directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Engineering (ENG), Social, Behavioral and Economic Sciences (SBE), and the Office of International Science and Engineering (OISE), to discuss how the global behavior of an infectious entity can emerge from the interactions that begin occurring between components at the molecular level and expand to physiological, environmental, and population scales. The workshop was divided into four sessions, each focusing on one of four different scales: 1) end-toend (or multi-scale) 2) molecular, 3) physiological and environmental, and 4) population and epidemiological. Particular focus was given to identifying challenges and opportunities in each of these domains. The workshop aimed to: • Identify interdisciplinary advances in science, technology, and human behavior to enable prediction and prevention of future pandemics • Begin to build the necessary convergence to be optimally prepared to prevent future pandemics • Establish convergent data commons and cyberinfrastructure for PIPP This workshop report summarizes the plenary presentations, panel discussions, and breakout group sessions that took place at this event. The results presented here are drawn from the viewpoints expressed by the participants and do not necessarily reflect those of the broader pandemic research community. 

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