Topic Domain Research Articles

The Centers for Disease Control and Prevention STRIVE Initiative: Construction of a National Program to Reduce Health Care-Associated Infections at the Local Level.

The Centers for Disease Control and Prevention STRIVE Initiative: Construction of a National Program to Reduce Health Care-Associated Infections at the Local Level.

Moving beyond classic readability formulas: new methods and new models

BackgroundAdvances in natural language processing (NLP) and computational linguistics have facilitated major improvements on traditional readability formulas that aim at predicting the overall difficulty of a text. Recent studies have identified several types of linguistic features that are theoretically motivated and predictive of human judgments of text readability, which outperform predictions made by traditional readability formulas, such as Flesch–Kincaid. The purpose of this study is to develop new readability models using advanced NLP tools to measure both text comprehension and reading speed.MethodsThis study used crowdsourcing techniques to collect human judgments of text comprehension and reading speed across a diverse variety of topic domains (science, technology and history). Linguistic features taken from state‐of‐the‐art NLP tools were used to develop models explaining human judgments of text comprehension and reading speed. The accuracy of these models was then compared with classic readability formulas.ResultsThe results indicated that models employing linguistic features more theoretically related to text comprehension and reading speed outperform classic readability models.ConclusionsThis study developed new readability formulas based on advanced NLP tools for both text comprehension and reading speed. These formulas, based on linguistic features that better represent theoretical and behavioural accounts of the reading process, significantly outperformed classic readability formulas.

Latin American consensus on the supportive management of patients with severe combined immunodeficiency

Severe combined immunodeficiency (SCID) represents the most lethal form of primary immunodeficiency, with mortality rates of greater than 90% within the first year of life without treatment. Hematopoietic stem cell transplantation and gene therapy are the only curative treatments available, and the best-known prognostic factors for success are age at diagnosis, age at hematopoietic stem cell transplantation, and the comorbidities that develop in between. There are no evidence-based guidelines for standardized clinical care for patients with SCID during the time between diagnosis and definitive treatment, and we aim to generate a consensus management strategy on the supportive care of patients with SCID. First, we gathered available information about SCID diagnostic and therapeutic guidelines, then we developed a document including diagnostic and therapeutic interventions, and finally we submitted the interventions for expert consensus through a modified Delphi technique. Interventions are grouped in 10 topic domains, including 123 "agreed" and 38 "nonagreed" statements. This document intends to standardize supportive clinical care of patients with SCID from diagnosis to definitive treatment, reduce disease burden, and ultimately improve prognosis, particularly in countries where newborn screening for SCID is not universally available and delayed diagnosis is the rule. Our work intends to provide a tool not only for immunologists but also for primary care physicians and other specialists involved in the care of patients with SCID.

The Tower of Babel problem: making data make sense with Basic Formal Ontology

PurposeApplied computational ontologies (ACOs) are increasingly used in data science domains to produce semantic enhancement and interoperability among divergent data. The purpose of this paper is to propose and implement a methodology for researching the sociotechnical dimensions of data-driven ontology work, and to show how applied ontologies are communicatively constituted with ethical implications.Design/methodology/approachThe underlying idea is to use a data assemblage approach for studying ACOs and the methods they use to add semantic complexity to digital data. The author uses a mixed methods approach, providing an analysis of the widely used Basic Formal Ontology (BFO) through digital methods and visualizations, and presents historical research alongside unstructured interview data with leading experts in BFO development.FindingsThe author found that ACOs are products of communal deliberation and decision making across institutions. While ACOs are beneficial for facilitating semantic data interoperability, ACOs may produce unintended effects when semantically enhancing data about social entities and relations. ACOs can have potentially negative consequences for data subjects. Further critical work is needed for understanding how ACOs are applied in contexts like the semantic web, digital platforms, and topic domains. ACOs do not merely reflect social reality through data but are active actors in the social shaping of data.Originality/valueThe paper presents a new approach for studying ACOs, the social impact of ACO work, and describes methods that may be used to produce further applied ontology studies.

A Graph Combination With Edge Pruning‐Based Approach for Author Name Disambiguation

Author name disambiguation (AND) is a challenging problem due to several issues such as missing key identifiers, same name corresponding to multiple authors, along with inconsistent representation. Several techniques have been proposed but maintaining consistent accuracy levels over all data sets is still a major challenge. We identify two major issues associated with the AND problem. First, the namesake problem in which two or more authors with the same name publishes in a similar domain. Second, the diverse topic problem in which one author publishes in diverse topical domains with a different set of coauthors. In this work, we initially propose a method named ATGEP for AND that addresses the namesake issue. We evaluate the performance of ATGEP using various ambiguous name references collected from the Arnetminer Citation (AC) and Web of Science (WoS) data set. We empirically show that the two aforementioned problems are crucial to address the AND problem that are difficult to handle using state‐of‐the‐art techniques. To handle the diverse topic issue, we extend ATGEP to a new variant named ATGEP‐web that considers external web information of the authors. Experiments show that with enough information available from external web sources ATGEP‐web can significantly improve the results further compared with ATGEP.

Automated Mandarin tone classification using deep neural networks trained on a large speech dataset

A tonal language is one in which the speaker’s intonation modifies the meaning of a word. In this work, we perform a rigorous analysis of intonation changes, or pitch contours, produced by native Mandarin speakers to predict the tone-contour type. Pitch contours are estimated using a number of different methods, also measuring each contour’s Mel-Frequency Cepstral Coefficients (MFCCs). The dataset used was autonomously generated from the Aishell open-source Mandarin speech corpus. Each sample was aligned with its transcript using Montreal Forced Alignment and segmented into individual words. The resulting corpus covers 11 topic domains, spoken by 400 individuals. Separate development, training, and testing datasets are created to ensure the integrity of our results. Pitch contours and their MFCCs are exposed to a number of machine learning techniques including clustered, regression, and traditional Deep Neural Network (DNN) approaches. MFCCs are additionally processed using convolutional neural networks. The models are used to predict the corresponding tone for a contour. Our work seeks to determine which intonation representations perform optimally for machine learning tasks. The tool is used to provide audio and visual feedback to learners of tonal languages. [Work supported by RPI Seed Grant and CISL].

Surgical and Perioperative Palliative Care: Updates from 2018 (TH307)

•Cite recently published literature on evidence and best practices for palliative care interventions in surgical patients.•Discuss areas of active surgical palliative care investigation in the context of common treatment dilemmas faced while caring for seriously ill patients with surgical disease.•Describe the application of surgical palliative care research and its impact on patient, family, and caregiver outcomes using concrete examples. Recent years have seen rapid expansion of the evidence base supporting the implementation of palliative care programs across health systems, ranging from the community to the intensive care unit. Surgical patients face uniquely complex decisions, significant symptom burden, and prognostic uncertainty, and their needs warrant surgery-specific palliative care delivery. Different models have been developed to address these needs, including communication training for surgical providers, embedded inpatient and outpatient surgical palliative care programs, improved prognostication models, and advanced care planning interventions. The core principles of surgery and anesthesiology closely mirror those of palliative care, and the intersection between them is characterized by an increasing annual volume of emerging research. This session will summarize landmark peer-reviewed papers on surgical palliative care published in 2018. The authors will search PubMed and hand-review key journals in surgery, anesthesiology, critical care, and palliative medicine to identify and select articles for inclusion based on journal impact factor and broad interest to the AAHPM/HPNA audience. Using a case-based format to provide clinical context, the panel will present literature drawn from the following topic domains: communication around surgical decision-making, frailty and surgical risk assessment, perioperative advanced care planning, specialty palliative care triggers and delivery models for surgical patients, innovative caregiver and clinician education strategies, and novel symptom management approaches applicable to surgical patient populations. The presenters consist of an HPM fellowship-trained practicing general surgeon and HPM-focused anesthesiologist, an HPM social worker with a specialized practice in surgical oncology and critical care, and an HPM physician whose clinical and academic interests are focused on surgical patient populations. The cross-specialty representation of the panelists ensures a diversity of perspectives that will enrich the audience’s appreciation for the role of surgical palliative care research in improving the outcomes of patients and families facing surgical illness across an array of care settings.

Analyzing divergent methodologies for political fact checking: United States and South Korea

ABSTRACTIn this work in progress, we explore methods employed by professional journalists to fact‐check political claims and debunk inaccurate (“fake”) news stories. We content‐analyzed 150 articles evaluating political claims and debunking fake news published by two media companies, the Washington Post (United States) and JTBC (South Korea), during the periods surrounding the recent presidential elections in their respective countries. Overall, the most common types of claims evaluated included false claims made by politicians or political groups about their opponents' positions and numerical and nonnumerical facts. The most frequently used methods for debunking such false claims included consulting with an independent expert in the given topic domain and checking government statistics or documents. There were some variations in the types of false claims focused and debunking methods used between the two fact‐checking organizations.

The Tower of Babel Problem: Making Data Make Sense with Basic Formal Ontology

Purpose – Applied computational ontologies (ACOs) are increasingly used in data science domains to produce semantic enhancement and interoperability among divergent data. The purpose of this paper is to propose and implement a methodology for researching the sociotechnical dimensions of data-driven ontology work, and to show how applied ontologies are communicatively constituted with ethical implications. Design/methodology/approach – The underlying idea is to use a data assemblage approach for studying ACOs and the methods they use to add semantic complexity to digital data. The author uses a mixed methods approach, providing a contextual review of the widely used Basic Formal Ontology (BFO) through digital methods and descriptions, and presents historical research alongside unstructured interview data with leading experts in BFO development. Findings – The author found that ACOs are products of communal deliberation and decision-making across institutions. While ACOs are beneficial for facilitating semantic data interoperability, ACOs may produce unintended effects when semantically enhancing data about social entities and relations. ACOs can have potentially negative consequences for data subjects. Further critical work is needed for understanding how ACOs are applied in contexts like the semantic web, digital platforms, and topic domains. ACOs do not merely reflect social reality through data but are active actors in the social shaping of data. Originality/value – The paper presents a new approach for studying ACOs, the unintended social effects of ACO work, and describes methods that may be used to produce further applied ontology studies.

Investigations into Data Ecosystems: a systematic mapping study

Data Ecosystems are socio-technical complex networks in which actors interact and collaborate with each other to find, archive, publish, consume, or reuse data as well as to foster innovation, create value, and support new businesses. While the Data Ecosystem field is thus arguably gaining in importance, research on this subject is still in its early stages of development. Up until now, not many academic papers related to Data Ecosystems have been published. Furthermore, to the best of our knowledge, there has been no systematic review of the literature on Data Ecosystems. In this study, we provide an overview of the current literature on Data Ecosystems by conducting a systematic mapping study. This study is intended to function as a snapshot of the research in the field and by doing so identifies the different definitions of Data Ecosystem and analyzes the evolution of Data Ecosystem research. The studies selected have been classified into categories related to the study method, contribution, research topic, and ecosystem domains. Finally, we analyze how Data Ecosystems are structured and organized, and what benefits can be expected from Data Ecosystems and what their limitations are.

Dental trauma in social media-Analysis of Facebook content and public engagement.

Social media sites such as Facebook are increasingly used to obtain healthcare-related information. The aim of this study was to examine Facebook content on dental trauma and to assess public engagement with this content. An anonymous account was created to search Facebook for information on dental trauma using the key words "dental trauma," "broken tooth," "tooth fracture," and "tooth trauma" over a period of 1year (from February 27, 2017, to February 27, 2018). The content was analyzed with regard to demographic characteristics, topic domains, and public engagement. A total of 395 Facebook pages and 149 groups were identified, but only one page and two groups met the inclusion criteria. There were 329 posts on dental trauma during the study period. More than half of the posts were event advertisements and personal highlights (56.2%), followed by posts mentioning articles in academia (17.9%). The average yearly engagement rate was 0.25%; news posts generated the highest engagement rate (0.76%), while academic posts generated the lowest engagement rate (0.16%). The singular post that generated the highest engagement rate (2.2%) was a news post referring to a dental trauma incident suffered by a professional football player. Only 14 posts (4.3%) contained information on prevention and immediate management of dental trauma. Facebook information on dental trauma is limited in quantity and quality. More emphasis should be placed on posting information suitable for the layperson and concerned with the basics of prevention and immediate management of dental trauma.

Common Topics of Publication and Levels of Evidence in the Current Hand Surgery Literature

Background Scientific publications are the primary vehicle for the distribution of scientific findings, but there has been limited research on literature topic surveillance. We sought to identify and characterize the most commonly published topic domains in the hand surgery literature.Methods We performed a 6-month hypothesis testing phase to identify the most frequently published topics in three hand surgery journals: Hand, The Journal of Hand Surgery (American), and The Journal of Hand Surgery (European). We reviewed all of the published articles in these journals from June 2010 to May 2015 to identify and characterize publications related to the three most common topic domains.Results A total of 2,146 articles were published during the 5-year study period. The three most frequent topics domains included distal radius (DR) (11% of all articles), flexor tendon (FT) (9%), and carpal tunnel (CT) (7.5%). These subjects accounted for a total of 584 articles (27% of all publications) and 3,014 published pages during the study period. FT, CT, and DR publications were cited on average 2.3 times per year (2.5, 2.4, and 2.0, respectively).Conclusion A small subset of topic domains makes up a significant proportion of scientific publications in hand surgery.

The Use of Support Vector Machines When Designing a User-Defined Niche Search Engine

<div>This study presents the construction of a niche search engine, whose search topic domain is to be user-defined.  The specific focus of this study is the investigation of the role that a Support Vector Machine plays when classifying textual data from web pages. Furthermore, the aim is to establish whether this niche search engine can return results that are more relevant to a user than when compared to those returned by a commercial search engine Through the conduction of various experiments across a number of appropriate datasets, the suitability of the SVM to classify web pages has been proven to meet the needs of a niche search engine. A subset of the most useful webpage-specific features has been discovered, with the best performing feature being a web pages’ Text & Title component. The user defined niche search engine was successfully designed and an experiment showed that it returned more relevant results than a commercial search engine.<div> </div></div>

Open-ended interview questions and saturation.

Sample size determination for open-ended questions or qualitative interviews relies primarily on custom and finding the point where little new information is obtained (thematic saturation). Here, we propose and test a refined definition of saturation as obtaining the most salient items in a set of qualitative interviews (where items can be material things or concepts, depending on the topic of study) rather than attempting to obtain all the items. Salient items have higher prevalence and are more culturally important. To do this, we explore saturation, salience, sample size, and domain size in 28 sets of interviews in which respondents were asked to list all the things they could think of in one of 18 topical domains. The domains—like kinds of fruits (highly bounded) and things that mothers do (unbounded)—varied greatly in size. The datasets comprise 20–99 interviews each (1,147 total interviews). When saturation was defined as the point where less than one new item per person would be expected, the median sample size for reaching saturation was 75 (range = 15–194). Thematic saturation was, as expected, related to domain size. It was also related to the amount of information contributed by each respondent but, unexpectedly, was reached more quickly when respondents contributed less information. In contrast, a greater amount of information per person increased the retrieval of salient items. Even small samples (n = 10) produced 95% of the most salient ideas with exhaustive listing, but only 53% of those items were captured with limited responses per person (three). For most domains, item salience appeared to be a more useful concept for thinking about sample size adequacy than finding the point of thematic saturation. Thus, we advance the concept of saturation in salience and emphasize probing to increase the amount of information collected per respondent to increase sample efficiency.

A quantitative exploration on reasons for citing articles from the perspective of cited authors

Citation is regarded as one of the “norms of science” (Merton in Am Sociol Rev 22(6):635–659, 1957) and is deeply researched by the field of scientometrics. The motivations authors have for citing one another are considered significant and have been the subject of extensive qualitative research such as content analysis, questionnaires, and interviews of citing authors. However, the existing qualitative studies have covered a limited number of samples. To expand the dataset, this paper proposes a quantitative method applied to detecting citation reasons from the angle of citation networks and the attributes of cited authors, including their publication count (the number of single-authored publications, collaborative and first-authored publications as well as collaborative but non-first-authored publications, and number of whole publications), citation count, research topic interests, and gender. By applying the Exponential Random Graph Models (ERGMs), the current study revealed that authors in the field of information retrieval tend to cite those with more single-authored, collaborative and first-authored, and collaborative but not first-authored publications. Besides, in this field, the number of publications, similar topical domains, and same gender are proven to be significantly favorable in selecting references in our experiment.

Twitter mining for ontology-based domain discovery incorporating machine learning

PurposeThis paper aims to obtain the domain of the textual content generated by users of online social network (OSN) platforms. Understanding a users’ domain (s) of interest is a significant step towards addressing their domain-based trustworthiness through an accurate understanding of their content in their OSNs.Design/methodology/approachThis study uses a Twitter mining approach for domain-based classification of users and their textual content. The proposed approach incorporates machine learning modules. The approach comprises two analysis phases: the time-aware semantic analysis of users’ historical content incorporating five commonly used machine learning classifiers. This framework classifies users into two main categories: politics-related and non-politics-related categories. In the second stage, the likelihood predictions obtained in the first phase will be used to predict the domain of future users’ tweets.FindingsExperiments have been conducted to validate the mechanism proposed in the study framework, further supported by the excellent performance of the harnessed evaluation metrics. The experiments conducted verify the applicability of the framework to an effective domain-based classification for Twitter users and their content, as evident in the outstanding results of several performance evaluation metrics.Research limitations/implicationsThis study is limited to an on/off domain classification for content of OSNs. Hence, we have selected a politics domain because of Twitter’s popularity as an opulent source of political deliberations. Such data abundance facilitates data aggregation and improves the results of the data analysis. Furthermore, the currently implemented machine learning approaches assume that uncertainty and incompleteness do not affect the accuracy of the Twitter classification. In fact, data uncertainty and incompleteness may exist. In the future, the authors will formulate the data uncertainty and incompleteness into fuzzy numbers which can be used to address imprecise, uncertain and vague data.Practical implicationsThis study proposes a practical framework comprising significant implications for a variety of business-related applications, such as the voice of customer/voice of market, recommendation systems, the discovery of domain-based influencers and opinion mining through tracking and simulation. In particular, the factual grasp of the domains of interest extracted at the user level or post level enhances the customer-to-business engagement. This contributes to an accurate analysis of customer reviews and opinions to improve brand loyalty, customer service, etc.Originality/valueThis paper fills a gap in the existing literature by presenting a consolidated framework for Twitter mining that aims to uncover the deficiency of the current state-of-the-art approaches to topic distillation and domain discovery. The overall approach is promising in the fortification of Twitter mining towards a better understanding of users’ domains of interest.

Correction to: An Interprofessional Framework for Telebehavioral Health Competencies

The Coalition for Technology in Behavioral Science (CTiBS) has developed an interprofessional, evidence-based, measurable framework for telebehavioral health (TBH) competencies. The paper presents the development of the CTiBS TBH framework, identifies the theoretical rationale and need for such competencies; outlines methods used; provides two detailed discussions of applications; presents a tabulized form of the framework, then discusses limitations and future research. In the CTiBS TBH competency framework, the term telebehavioral health is meant to include telemental health and e-therapy. The TBH competency framework is offered as an initial working document to identify and organize discreet, measurable telebehavioral practices derived from a review of the literature, technological advances and day-to-day clinical practice. It reflects core knowledge, skills and attitudes needed for competent telebehavioral health practice. The competency framework is directly applicable to psychiatry/medicine, psychology, social work, counseling, marriage/family, behavior analysis and other behavioral sciences. The CTiBS TBH framework organizes seven topic domains and five subdomains according to competency level i.e., Novice, Proficient or Authority. In turn, each competency level is categorized into 51 discrete telebehavioral objectives, which are then distinguished by 149 cumulative and measurable telebehavioral practices. The seven TBH competency domains identified by CTiBS include: 1) Clinical Evaluation & Care, with three subdomains addressing Assessment & Treatment, Cultural Competence & Diversity and Documentation & Administrative Procedures; 2) Virtual Environment & Telepresence; 3) Technology; 4) Legal & Regulatory Issues; 5) Evidence-Based & Ethical Practice, with two subdomains addressing Standards and Guidelines and Social Media; 6) Mobile Health and Apps and 7) Telepractice Development.

A Topic Domain Identification Model Combining Local and Global Characteristics

A Topic Domain Identification Model Combining Local and Global Characteristics

Digital Epidemiology: Use of Digital Data Collected for Non-epidemiological Purposes in Epidemiological Studies

ObjectivesWe reviewed digital epidemiological studies to characterize how researchers are using digital data by topic domain, study purpose, data source, and analytic method.MethodsWe reviewed research articles published within the last decade that used digital data to answer epidemiological research questions. Data were abstracted from these articles using a data collection tool that we developed. Finally, we summarized the characteristics of the digital epidemiological studies.ResultsWe identified six main topic domains: infectious diseases (58.7%), non-communicable diseases (29.4%), mental health and substance use (8.3%), general population behavior (4.6%), environmental, dietary, and lifestyle (4.6%), and vital status (0.9%). We identified four categories for the study purpose: description (22.9%), exploration (34.9%), explanation (27.5%), and prediction and control (14.7%). We identified eight categories for the data sources: web search query (52.3%), social media posts (31.2%), web portal posts (11.9%), webpage access logs (7.3%), images (7.3%), mobile phone network data (1.8%), global positioning system data (1.8%), and others (2.8%). Of these, 50.5% used correlation analyses, 41.3% regression analyses, 25.6% machine learning, and 19.3% descriptive analyses.ConclusionsDigital data collected for non-epidemiological purposes are being used to study health phenomena in a variety of topic domains. Digital epidemiology requires access to large datasets and advanced analytics. Ensuring open access is clearly at odds with the desire to have as little personal data as possible in these large datasets to protect privacy. Establishment of data cooperatives with restricted access may be a solution to this dilemma.

An Interprofessional Framework for Telebehavioral Health Competencies

The Coalition for Technology in Behavioral Science (CTiBS) has developed an interprofessional, evidence-based, measurable framework for telebehavioral health (TBH) competencies. The paper presents the development of the CTiBS TBH framework, identifies the theoretical rationale and need for such competencies, outlines methods used, provides two detailed discussions of applications, presents a tabulized form of the framework, and then discusses limitations and future research. In the CTiBS TBH competency framework, the term telebehavioral health is meant to include telemental health and e-therapy. The TBH competency framework is offered as an initial working document to identify and organize discreet, measurable telebehavioral practices derived from a review of the literature, technological advances, and day-to-day clinical practice. It reflects core knowledge, skills, and attitudes needed for competent telebehavioral health practice. The competency framework is directly applicable to psychiatry/medicine, psychology, social work, counseling, marriage/family, behavior analysis, and other behavioral sciences. The CTiBS TBH framework organizes seven topic domains and five subdomains according to competency level, i.e., Novice, Proficient, or Authority. In turn, each competency level is categorized into 51 discrete telebehavioral objectives, which are then distinguished by 149 cumulative and measurable telebehavioral practices. The seven TBH competency domains identified by CTiBS include (1) Clinical Evaluation and Care, with three subdomains addressing Assessment and Treatment, Cultural Competence and Diversity, and Documentation and Administrative Procedures; (2) Virtual Environment and Telepresence; (3) Technology; (4) Legal and Regulatory Issues; (5) Evidence-Based and Ethical Practice, with two subdomains addressing Standards and Guidelines and Social Media; (6) Mobile Health and Apps; and (7) Telepractice Development.