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Many disciplines, including the broad Field of Information (iField), offer Data Science (DS) programs. There have been significant efforts exploring an individual discipline's identity and unique contributions to the broader DS education landscape. To advance DS education in the iField, the iSchool Data Science Curriculum Committee (iDSCC) was formed and charged with building and recommending a DS education framework for iSchools. This paper reports on the research process and findings of a series of studies to address important questions: What is the iField identity in the multidisciplinary DS education landscape? What is the status of DS education in iField schools? What knowledge and skills should be included in the core curriculum for iField DS education? What are the jobs available for DS graduates from the iField? What are the differences between graduate‐level and undergraduate‐level DS education? Answers to these questions will not only distinguish an iField approach to DS education but also define critical components of DS curriculum. The results will inform individual DS programs in the iField to develop curriculum to support undergraduate and graduate DS education in their local context. [ABSTRACT FROM AUTHOR]

Objective: Biomedical text summarization helps biomedical information seekers avoid information overload by reducing the length of a document while preserving the contents' essence. Our systematic review investigates the most recent biomedical text summarization researches on biomedical literature and electronic health records by analyzing their techniques, areas of application, and evaluation methods. We identify gaps and propose potential directions for future research.Materials and Methods: This review followed the PRISMA methodology and replicated the approaches adopted by the previous systematic review published on the same topic. We searched 4 databases (PubMed, ACM Digital Library, Scopus, and Web of Science) from January 1, 2013 to April 8, 2021. Two reviewers independently screened title, abstract, and full-text for all retrieved articles. The conflicts were resolved by the third reviewer. The data extraction of the included articles was in 5 dimensions: input, purpose, output, method, and evaluation.Results: Fifty-eight out of 7235 retrieved articles met the inclusion criteria. Thirty-nine systems used single-document biomedical research literature as their input, 17 systems were explicitly designed for clinical support, 47 systems generated extractive summaries, and 53 systems adopted hybrid methods combining computational linguistics, machine learning, and statistical approaches. As for the assessment, 51 studies conducted an intrinsic evaluation using predefined metrics.Discussion and Conclusion: This study found that current biomedical text summarization systems have achieved good performance using hybrid methods. Studies on electronic health records summarization have been increasing compared to a previous survey. However, the majority of the works still focus on summarizing literature. [ABSTRACT FROM AUTHOR]

Objective: This article reviews recent literature on the use of SNOMED CT as an extension of Lee et al's 2014 review on the same topic. The Lee et al's article covered literature published from 2001-2012, and the scope of this review was 2013-2020.Materials and Methods: In line with Lee et al's methods, we searched the PubMed and Embase databases and identified 1002 articles for review, including studies from January 2013 to September 2020. The retrieved articles were categorized and analyzed according to SNOMED CT focus categories (ie, indeterminate, theoretical, pre-development, implementation, and evaluation/commodity), usage categories (eg, illustrate terminology systems theory, prospective content coverage, used to classify or code in a study, retrieve or analyze patient data, etc.), medical domains, and countries.Results: After applying inclusion and exclusion criteria, 622 articles were selected for final review. Compared to the papers published between 2001 and 2012, papers published between 2013 and 2020 revealed an increase in more mature usage of SNOMED CT, and the number of papers classified in the "implementation" and "evaluation/commodity" focus categories expanded. When analyzed by decade, papers in the "pre-development," "implementation," and "evaluation/commodity" categories were much more numerous in 2011-2020 than in 2001-2010, increasing from 169 to 293, 30 to 138, and 3 to 65, respectively.Conclusion: Published papers in more mature usage categories have substantially increased since 2012. From 2013 to present, SNOMED CT has been increasingly implemented in more practical settings. Future research should concentrate on addressing whether SNOMED CT influences improvement in patient care. [ABSTRACT FROM AUTHOR]

Purpose: Electronic health records (EHR) can enable collection and use of data for achieving better health both at the patient and population health levels. The World Health Organization's (WHO) draft 2019 four-year global digital health strategy aims to "improve health for everyone, everywhere by accelerating the adoption of appropriate digital health" and EHRs are key to achieving better health goals. Despite the fact that EHRs can help to achieve better health, there is lack of evidence explaining national and sub-national EHR development in the limited resource settings. Design/methodology/approach: We conducted a landscape study to describe the EHR development and use in the low- and middle-income countries for achieving better health. We reviewed literature from four scientific databases and analyzed gray literature identified in consultation with 17 international experts. Findings: The findings of this literature review are presented in three subsections. The first two subsections describe key stakeholders for development of national and sub-national EHR and health information architecture which includes status of ehealth foundations, EHR, and sub-systems in the country. The third subsection presents and discusses key challenges related to sustainability of national and sub-national EHRs. The findings in these three subsections are further explored through examples of health information flow in Uganda, and electronic medical record/EHR implementation in Sierra Leone and Malawi. These examples briefly describe stakeholders, information architecture, and sustainability challenges. Originality/value: This paper fills an important research gap and clearly explains the urgent research need to build context-specific EHR development models to enable use of data for better health. [ABSTRACT FROM AUTHOR]

Objective was to assess the relationship between task demand, mental effort, task difficulty, and performance during physicians' interaction with electronic health records (EHRs). Seventeen physicians performed three EHR-based scenarios with varying task demands. Mental effort was measured using eye tracking measures via task evoked pupillary responses (TEPR), blink frequency, and gaze speed; task difficulty (or user behavior) was measured using frequent mouse click patterns and task flow; user performance was quantified using two types of omission errors: (i) omission errors with no evidence of trying to complete the task and (ii) omission error with evidence of trying but unable to complete the task. The results indicated that task demand significantly increased mental effort, but not task difficulty. Task demand, mental effort, and task difficulty all predicted performance. Specifically, there was a significant relationship between (i) task demand, TEPR and omission errors with no evidence of trying to complete the task, and (ii) blink frequency, repeated search clicks and omission error with evidence of trying but unable to complete the task. In concert, results suggest that physicians' performance during EHR interaction was negatively affected by task demands and increase in mental effort. This highlight the need for implementation of appropriate quality assurance (QA) measures, in addition to EHR usability improvement, to minimize omission errors and improve physician's performance. Additionally, the lack of relationship between task demand and task difficulty highlights a need for further methodological and empirical studies to advance our understanding from theory to application during physician--EHR interaction. [ABSTRACT FROM AUTHOR]

Online knowledge resources such as Medline can address most clinicians' patient care information needs. Yet, significant barriers, notably lack of time, limit the use of these sources at the point of care. The most common information needs raised by clinicians are treatment-related. Comparative effectiveness studies allow clinicians to consider multiple treatment alternatives for a particular problem. Still, solutions are needed to enable efficient and effective consumption of comparative effectiveness research at the point of care. Objective: Design and assess an algorithm for automatically identifying comparative effectiveness studies and extracting the interventions investigated in these studies. Methods: The algorithm combines semantic natural language processing, Medline citation metadata, and machine learning techniques. We assessed the algorithm in a case study of treatment alternatives for depression. Results: Both precision and recall for identifying comparative studies was 0.83. A total of 86% of the interventions extracted perfectly or partially matched the gold standard. Conclusion: Overall, the algorithm achieved reasonable performance. The method provides building blocks for the automatic summarization of comparative effectiveness research to inform point of care decision-making. [ABSTRACT FROM AUTHOR]

Objective Online health knowledge resources contain answers to most of the information needs raised by clinicians in the course of care. However, significant barriers limit the use of these resources for decision-making, especially clinicians' lack of time. In this study we assessed the feasibility of automatically generating knowledge summaries for a particular clinical topic composed of relevant sentences extracted from Medline citations. Methods The proposed approach combines information retrieval and semantic information extraction techniques to identify relevant sentences from Medline abstracts. We assessed this approach in two case studies on the treatment alternatives for depression and Alzheimer's disease. Results A total of 515 of 564 (91.3%) sentences retrieved in the two case studies were relevant to the topic of interest. About one-third of the relevant sentences described factual knowledge or a study conclusion that can be used for supporting information needs at the point of care. Conclusions The high rate of relevant sentences is desirable, given that clinicians' lack of time is one of the main barriers to using knowledge resources at the point of care. Sentence rank was not significantly associated with relevancy, possibly due to most sentences being highly relevant. Sentences located closer to the end of the abstract and sentences with treatment and comparative predications were likely to be conclusive sentences. Our proposed technical approach to helping clinicians meet their information needs is promising. The approach can be extended for other knowledge resources and information need types. [ABSTRACT FROM AUTHOR]

Clinical data have tremendous value for translational research, but only if security and privacy concerns can be addressed satisfactorily. A collaboration of clinical and informatics teams, including RENCI, NC TraCS, UNC's School of Information and Library Science, Information Technology Service's Research Computing and other partners at the University of North Carolina at Chapel Hill have developed a system called the Secure Medical Research Workspace (SMRW) that enables researchers to use clinical data securely for research. SMRW significantly minimizes the risk presented when using identified clinical data, thereby protecting patients, researchers, and institutions associated with the data. The SMRW is built on a novel combination of virtualization and data leakage protection and can be combined with other protection methodologies and scaled to production levels. [ABSTRACT FROM AUTHOR]

With the ubiquitous production, distribution and consumption of information, today's digital environments such as the Web are increasingly large and decentralized. It is hardly possible to obtain central control over information collections and systems in these environments. Searching for information in these information spaces has brought about problems beyond traditional boundaries of information retrieval (IR) research. This article addresses one important aspect of scalability challenges facing information retrieval models and investigates a decentralized, organic view of information systems pertaining to search in large-scale networks. Drawing on observations from earlier studies, we conduct a series of experiments on decentralized searches in large-scale networked information spaces. Results show that how distributed systems interconnect is crucial to retrieval performance and scalability of searching. Particularly, in various experimental settings and retrieval tasks, we find a consistent phenomenon, namely, the Clustering Paradox, in which the level of network clustering (semantic overlay) imposes a scalability limit. Scalable searches are well supported by a specific, balanced level of network clustering emerging from local system interconnectivity. Departure from that level, either stronger or weaker clustering, leads to search performance degradation, which is dramatic in large-scale networks. [ABSTRACT FROM AUTHOR]

Ontology development and maintenance is a costly undertaking, despite known benefits. Empowerment via ownership may offer a solution to this problem. This article considers empowering clinical scientists as ontology engineers and illustrates this concept through an account of an ontology project in Chronic Obstruction Pulmonary Disease (COPD). The article begins with a brief review of ontology and clinical science. Next, the SPIROMICS project is introduced and the inter-workings (nuts-and-bolts) of the current COPD ontology are described, including an overview of the ontology development team's accomplishments. Following is an initial high-level proposal of steps to engage clinical scientists in the COPD work-as engineers, as well as some processing guidelines. The final section presents conclusions and highlights next steps. [ABSTRACT FROM AUTHOR]

Many digital library topics may be taught using digital library applications, in the context of project-based digital library courses. Several digital library applications exist, and these applications are used as teaching tools to illustrate the functionality of digital libraries as well as the design decisions that go into them. Using digital library applications as teaching tools provides a valuable learning experience for students, and may provide useful feedback to the developers of DL applications. This paper identifies and explores DL topics that may most effectively be taught using DL applications, in the context of project-based DL courses. [ABSTRACT FROM AUTHOR]

A library user searching for high-quality and authoritative information today is confronted with thousands of resources that cover a wide variety of topics. The heterogeneity factor alone can be a major obstacle for the user to select appropriate resources to search. Depending on the information need, the user may have to navigate among resources that are in different formats , have different levels of coverage, or are published in different languages. In this phase, users must select one or more resources as target for their queries. Despite the fact that most libraries provide access to numerous databases, very few document interfaces have integrated functions to help users select a subset of databases that is likely to cover information related to their queries. Faced with choices among numerous resources, the user may need guidance during the formulation phase in selecting appropriate vocabularies or ways to restrict search results. Choice of operators and constraints, however, can differ significantly depending on the type of database the user selects.

There has been considerable interest in recent years in providing automated information services, such as information classification, by means of a society of collaborative agents. These agents augment each other's knowledge structures (e.g., the vocabularies) and assist each other in providing efficient information services to a human user. However, when the number of agents present in the society increases, exhaustive communication and collaboration among agents result in a large communication overhead and increased delays in response time. This paper introduces a method to achieve selective interaction with a relatively small number of potentially useful agents, based on simple agent modeling and acquaintance lists. The key idea presented here is that the acquaintance list of an agent, representing a small number of other agents to be collaborated with, is dynamically adjusted. The best acquaintances are automatically discovered using a learning algorithm, based on the past history of collaboration. Experimental results are presented to demonstrate that such dynamically learned acquaintance lists can lead to high quality of classification, while significantly reducing the delay in response time. [ABSTRACT FROM AUTHOR]

Introduces a series of articles concerning information retrieval system. Intelligent navigation aids to Web site visitors; Application of a hybrid strategy for generating interest representation, combining users' personal transactions with peer-group-level transaction patterns for an e-commerce site; Dynamic adaptation of online catalogs.

Develops a shift detection model based on a Bayesian framework to address the issue of tracking user interest profile in information filtering systems. Text document filtering system; Relationship between degree of shift, cost of detection error and time needed for detection; Performance evaluation of the Bayesian shift detection model.

The impact of the World Wide Web on providing an easy information access is clearly evident in all aspects of today's life. As attractive as the information availability is, due to its sheer volume, it creates an information-overload on users. Agent-based collaborative filtering is a technique used to effectively counter this burden. For agents to collaborate successfully, and maintain an overall progress of the entire interconnected environment, a governing mechanism is necessary. In this article, we present an economic framework based on a bidding mechanism for agents to communicate and negotiate with each other, thereby achieving collaborative information classification. D-SIFTER, a system developed using this economic framework, is described along with various experiments and their results. [ABSTRACT FROM AUTHOR]

This article presents a novel visual analytics (VA)-based clinical decision support (CDS) tool prototype that was designed as a collaborative work between Renaissance Computing Institute and Duke University. Using Major Depressive Disorder data from MindLinc electronic health record system at Duke, the CDS tool shows an approach to leverage data from comparative population (patients with similar medical profile) to enhance a clinicians' decision making process at the point of care. The initial work is being extended in collaboration with the University of North Carolina CTSA to address the key challenges of CDS, as well as to show the use of VA to derive insight from large volumes of Electronic Health Record patient data. Clin Trans Sci 2011; Volume 4: 369-371 [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses article within the issue on topics including the aspects of delayed recognition in the science context, the awakening probability function, and the citation analysis.