A retrospective case study of successful translational research: Cardiovascular disease risk assessment, experiences in community engagement.

In underserved communities across New York City, uninsured adults encounter a greater risk of cardiovascular disease (CVD) and diabetes. The Heart-to-Heart Community Outreach Program (H2H) addresses these disparities by screening for CVD risk factors, identifying healthcare access barriers, and fostering community engagement in translational research at the Weill Cornell Medicine Clinical and Translational Science Award (CTSA) hub. Screening events are hosted in partnership with faith-based institutions. Participants provide a medical history, complete a survey, and receive counseling by clinicians with referrals for follow-up care. This study aims to quantify H2H screening participant health status; identify socioeconomic, health access, and health-related barriers disproportionately promoting the onset of CVD and diabetes; and develop long-term community partnerships to enable underserved communities to influence activities across the translational research spectrum at our CTSA hub. The population served is disproportionately non-white, and uninsured, with many low-income and underserved individuals. The program was developed in partnership with our Community Advisory Board to empower this cohort to make beneficial lifestyle changes. Leveraging partnerships with faith-based institutions and community centers in at-risk New York City neighborhoods, H2H addresses the increasing burden of diabetes and CVD risk factors in vulnerable individuals while promoting community involvement in CTSA activities, serving as a model for similar initiatives.

Figure Correction: Using Social Media to Help Understand Patient-Reported Health Outcomes of Post-COVID-19 Condition: Natural Language Processing Approach.

[This corrects the article DOI: 10.2196/45767.].

A Retrospective Case Study of Successful Translational Research: Cardiovascular Disease Risk Assessment, Experiences in Community Engagement.

In underserved communities in New York City, uninsured adults encounter a greater risk of cardiovascular disease and diabetes. The Heart-to-Heart Community Outreach Program (H2H) is addressing these disparities by providing screenings for diabetes and other cardiovascular disease risk factors, fostering community engagement in translational research at our CTSC. Screening events are hosted in partnership with community faith-based institutions. Participants provide medical history, complete a survey, and receive individualized counseling by clinicians with referrals for follow-up care. The population served is disproportionately non-white, uninsured, with low-income, and underserved. The program empowers participants to make beneficial lifestyle changes using myriad strategies to reach those most in need. This required strong foundational program leadership, effective inter-institutional collaboration, and maintaining of community trust. Leveraging partnerships with faith-based institutions and community centers in at-risk NYC neighborhoods, H2H addresses the increasing burden of diabetes and cardiovascular disease risk factors in vulnerable individuals and provides a model for similar initiatives.

Using Social Media to Help Understand Patient-Reported Health Outcomes of Post-COVID-19 Condition: Natural Language Processing Approach.

BACKGROUND: While scientific knowledge of post-COVID-19 condition (PCC) is growing, there remains significant uncertainty in the definition of the disease, its expected clinical course, and its impact on daily functioning. Social media platforms can generate valuable insights into patient-reported health outcomes as the content is produced at high resolution by patients and caregivers, representing experiences that may be unavailable to most clinicians. OBJECTIVE: In this study, we aimed to determine the validity and effectiveness of advanced natural language processing approaches built to derive insight into PCC-related patient-reported health outcomes from social media platforms Twitter and Reddit. We extracted PCC-related terms, including symptoms and conditions, and measured their occurrence frequency. We compared the outputs with human annotations and clinical outcomes and tracked symptom and condition term occurrences over time and locations to explore the pipeline's potential as a surveillance tool. METHODS: We used bidirectional encoder representations from transformers (BERT) models to extract and normalize PCC symptom and condition terms from English posts on Twitter and Reddit. We compared 2 named entity recognition models and implemented a 2-step normalization task to map extracted terms to unique concepts in standardized terminology. The normalization steps were done using a semantic search approach with BERT biencoders. We evaluated the effectiveness of BERT models in extracting the terms using a human-annotated corpus and a proximity-based score. We also compared the validity and reliability of the extracted and normalized terms to a web-based survey with more than 3000 participants from several countries. RESULTS: UmlsBERT-Clinical had the highest accuracy in predicting entities closest to those extracted by human annotators. Based on our findings, the top 3 most commonly occurring groups of PCC symptom and condition terms were systemic (such as fatigue), neuropsychiatric (such as anxiety and brain fog), and respiratory (such as shortness of breath). In addition, we also found novel symptom and condition terms that had not been categorized in previous studies, such as infection and pain. Regarding the co-occurring symptoms, the pair of fatigue and headaches was among the most co-occurring term pairs across both platforms. Based on the temporal analysis, the neuropsychiatric terms were the most prevalent, followed by the systemic category, on both social media platforms. Our spatial analysis concluded that 42% (10,938/26,247) of the analyzed terms included location information, with the majority coming from the United States, United Kingdom, and Canada. CONCLUSIONS: The outcome of our social media-derived pipeline is comparable with the results of peer-reviewed articles relevant to PCC symptoms. Overall, this study provides unique insights into patient-reported health outcomes of PCC and valuable information about the patient's journey that can help health care providers anticipate future needs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1101/2022.12.14.22283419.

ReCiter: An open source, identity-driven, authorship prediction algorithm optimized for academic institutions.

Academic institutions need to maintain publication lists for thousands of faculty and other scholars. Automated tools are essential to minimize the need for direct feedback from the scholars themselves who are practically unable to commit necessary effort to keep the data accurate. In relying exclusively on clustering techniques, author disambiguation applications fail to satisfy key use cases of academic institutions. Algorithms can perfectly group together a set of publications authored by a common individual, but, for them to be useful to an academic institution, they need to programmatically and recurrently map articles to thousands of scholars of interest en masse. Consistent with a savvy librarian's approach for generating a scholar's list of publications, identity-driven authorship prediction is the process of using information about a scholar to quantify the likelihood that person wrote certain articles. ReCiter is an application that attempts to do exactly that. ReCiter uses institutionally-maintained identity data such as name of department and year of terminal degree to predict which articles a given scholar has authored. To compute the overall score for a given candidate article from PubMed (and, optionally, Scopus), ReCiter uses: up to 12 types of commonly available, identity data; whether other members of a cluster have been accepted or rejected by a user; and the average score of a cluster. In addition, ReCiter provides scoring and qualitative evidence supporting why particular articles are suggested. This context and confidence scoring allows curators to more accurately provide feedback on behalf of scholars. To help users to more efficiently curate publication lists, we used a support vector machine analysis to optimize the scoring of the ReCiter algorithm. In our analysis of a diverse test group of 500 scholars at an academic private medical center, ReCiter correctly predicted 98% of their publications in PubMed.

Application of Social Network Analysis of COVID-19 Related Tweets Mentioning Cannabis and Opioids to Gain Insights for Drug Abuse Research.

We applied social network analysis (SNA) to Tweets mentioning cannabis or opioid-related terms to publicly available COVID-19 related Tweets collected from Jan 21st to May 3rd, 2020 (n= 2,558,474 Tweets). We randomly extracted 16,154 Tweets mentioning cannabis and 4,670 Tweets mentioning opioids from the COVID-19 Tweet corpora for our analysis. The cannabis related Tweets created by 6,144 users were disseminated to 280,042,783 users and retweeted 11 times the number of original messages while opioid-related Tweets created by 3,412 users were disseminated to smaller number of users. The opioids Twitter network showed more cohesive online group activities and a cleaner online environment with less disinformation. The cannabis Twitter network showed a less desirable online environment with more disinformation (false information to mislead the public) and stakeholders lacking strong science knowledge. Application of SNA to Tweets provides insights for future online-based drug abuse research during the outbreak.

Application of Topic Modeling to Tweets as the Foundation for Health Disparity Research for COVID-19.

We randomly extracted publicly available Tweets mentioning COVID-19 related terms (n=2,558,474 Tweets) from Tweet corpora collected daily using an API from Jan 21st to May 3rd, 2020. We applied a clustering algorithm to publicly available Tweets authored by African Americans (n=1,763) to detect topics and sentiment applying natural language processing (NLP). We visualized fifteen topics (four themes) using network diagrams (Newman modularity 0.74). Compared to the COVID-19 related Tweets authored by others, positive sentiments, cohesively encouraging online discussions (e.g., Black strong 27.1%, growing up Blacks 22.8%, support Black business 17.0%, how to build resilience 7.8%), and COVID-19 prevention behaviors (e.g., masks 4.7%, encouraging social distancing 9.4%) were uniquely observed in African American Twitter communities. Application of topic modeling techniques to streaming social media Twitter provides the foundation for research team insights regarding information and future virtual based intervention and social media based health disparity research for COVID-19.

Applying Artificial Intelligence to Predict Self-Reported Poor Health Among Black and Hispanic Caregivers with Mild Cognitive Impairment.

We applied artificial intelligence techniques to build correlate models that predict general poor health in a national sample of caregivers with mild cognitive impairment (MCI). Our application of deep learning identified age, duration of caregiving, amount of alcohol intake, weight, myocardial infarction (MI) and frequency of MCI symptoms for Blacks and Hispanics whereas frequency of MCI symptoms, income, weight, coronary heart disease (CHD), age, and use of e-cigarette for the others as the strongest correlates of poor health among 81 variables entered. The application of artificial intelligence efficiently provided intervention strategies for Black and Hispanic caregivers with MCI.

Research evaluation support services in biomedical libraries.

Objective: The paper provides a review of current practices related to evaluation support services reported by seven biomedical and research libraries. Methods: A group of seven libraries from the United States and Canada described their experiences with establishing evaluation support services at their libraries. A questionnaire was distributed among the libraries to elicit information as to program development, service and staffing models, campus partnerships, training, products such as tools and reports, and resources used for evaluation support services. The libraries also reported interesting projects, lessons learned, and future plans. Results: The seven libraries profiled in this paper report a variety of service models in providing evaluation support services to meet the needs of campus stakeholders. The service models range from research center cores, partnerships with research groups, and library programs with staff dedicated to evaluation support services. A variety of products and services were described such as an automated tool to develop rank-based metrics, consultation on appropriate metrics to use for evaluation, customized publication and citation reports, resource guides, classes and training, and others. Implementing these services has allowed the libraries to expand their roles on campus and to contribute more directly to the research missions of their institutions. Conclusions: Libraries can leverage a variety of evaluation support services as an opportunity to successfully meet an array of challenges confronting the biomedical research community, including robust efforts to report and demonstrate tangible and meaningful outcomes of biomedical research and clinical care. These services represent a transformative direction that can be emulated by other biomedical and research libraries.

Sometimes more is more: iterative participatory design of infographics for engagement of community members with varying levels of health literacy.

OBJECTIVE: To collaborate with community members to develop tailored infographics that support comprehension of health information, engage the viewer, and may have the potential to motivate health-promoting behaviors. METHODS: The authors conducted participatory design sessions with community members, who were purposively sampled and grouped by preferred language (English, Spanish), age group (18-30, 31-60, >60 years), and level of health literacy (adequate, marginal, inadequate). Research staff elicited perceived meaning of each infographic, preferences between infographics, suggestions for improvement, and whether or not the infographics would motivate health-promoting behavior. Analysis and infographic refinement were iterative and concurrent with data collection. RESULTS: Successful designs were information-rich, supported comparison, provided context, and/or employed familiar color and symbolic analogies. Infographics that employed repeated icons to represent multiple instances of a more general class of things (e.g., apple icons to represent fruit servings) were interpreted in a rigidly literal fashion and thus were unsuitable for this community. Preliminary findings suggest that infographics may motivate health-promoting behaviors. DISCUSSION: Infographics should be information-rich, contextualize the information for the viewer, and yield an accurate meaning even if interpreted literally. CONCLUSION: Carefully designed infographics can be useful tools to support comprehension and thus help patients engage with their own health data. Infographics may contribute to patients' ability to participate in the Learning Health System through participation in the development of a robust data utility, use of clinical communication tools for health self-management, and involvement in building knowledge through patient-reported outcomes.

Associating co-authorship patterns with publications in high-impact journals.

OBJECTIVES: To develop a method for investigating co-authorship patterns and author team characteristics associated with the publications in high-impact journals through the integration of public MEDLINE data and institutional scientific profile data. METHODS: For all current researchers at Columbia University Medical Center, we extracted their publications from MEDLINE authored between years 2007 and 2011 and associated journal impact factors, along with author academic ranks and departmental affiliations obtained from Columbia University Scientific Profiles (CUSP). Chi-square tests were performed on co-authorship patterns, with Bonferroni correction for multiple comparisons, to identify team composition characteristics associated with publication impact factors. We also developed co-authorship networks for the 25 most prolific departments between years 2002 and 2011 and counted the internal and external authors, inter-connectivity, and centrality of each department. RESULTS: Papers with at least one author from a basic science department are significantly more likely to appear in high-impact journals than papers authored by those from clinical departments alone. Inclusion of at least one professor on the author list is strongly associated with publication in high-impact journals, as is inclusion of at least one research scientist. Departmental and disciplinary differences in the ratios of within- to outside-department collaboration and overall network cohesion are also observed. CONCLUSIONS: Enrichment of co-authorship patterns with author scientific profiles helps uncover associations between author team characteristics and appearance in high-impact journals. These results may offer implications for mentoring junior biomedical researchers to publish on high-impact journals, as well as for evaluating academic progress across disciplines in modern academic medical centers.

Automatic generation of investigator bibliographies for institutional research networking systems.

OBJECTIVE: Publications are a key data source for investigator profiles and research networking systems. We developed ReCiter, an algorithm that automatically extracts bibliographies from PubMed using institutional information about the target investigators. METHODS: ReCiter executes a broad query against PubMed, groups the results into clusters that appear to constitute distinct author identities and selects the cluster that best matches the target investigator. Using information about investigators from one of our institutions, we compared ReCiter results to queries based on author name and institution and to citations extracted manually from the Scopus database. Five judges created a gold standard using citations of a random sample of 200 investigators. RESULTS: About half of the 10,471 potential investigators had no matching citations in PubMed, and about 45% had fewer than 70 citations. Interrater agreement (Fleiss' kappa) for the gold standard was 0.81. Scopus achieved the best recall (sensitivity) of 0.81, while name-based queries had 0.78 and ReCiter had 0.69. ReCiter attained the best precision (positive predictive value) of 0.93 while Scopus had 0.85 and name-based queries had 0.31. DISCUSSION: ReCiter accesses the most current citation data, uses limited computational resources and minimizes manual entry by investigators. Generation of bibliographies using named-based queries will not yield high accuracy. Proprietary databases can perform well but requite manual effort. Automated generation with higher recall is possible but requires additional knowledge about investigators.

Researchermap: a tool for visualizing author locations using Google maps.

We hereby present ResearcherMap, a tool to visualize locations of authors of scholarly papers. In response to a query, the system returns a map of author locations. To develop the system we first populated a database of author locations, geocoding institution locations for all available institutional affiliation data in our database. The database includes all authors of Medline papers from 1990 to 2012. We conducted a formative heuristic usability evaluation of the system and measured the system's accuracy and performance. The accuracy of finding the accurate address is 97.5% in our system.

Method for the development of data visualizations for community members with varying levels of health literacy.

Many Americans are challenged by the tasks of understanding and acting upon their own health data. Low levels of health literacy contribute to poor comprehension and undermine the confidence necessary for health self-management. Visualizations are useful for minimizing comprehension gaps when communicating complex quantitative information. The process of developing visualizations that accommodate the needs of individuals with varying levels of health literacy remains undefined. In this paper we provide detailed descriptions of a) an iterative methodological approach to the development of visualizations, b) the resulting types of visualizations and examples thereof, and c) the types of data the visualizations will be used to convey. We briefly describe subsequent phases in which the visualizations will be tested and refined. Web deployment of the final visualizations will support the ethical obligation to return the data to the research participants and community that contributed it.

A brain region-specific predictive gene map for autism derived by profiling a reference gene set.

Molecular underpinnings of complex psychiatric disorders such as autism spectrum disorders (ASD) remain largely unresolved. Increasingly, structural variations in discrete chromosomal loci are implicated in ASD, expanding the search space for its disease etiology. We exploited the high genetic heterogeneity of ASD to derive a predictive map of candidate genes by an integrated bioinformatics approach. Using a reference set of 84 Rare and Syndromic candidate ASD genes (AutRef84), we built a composite reference profile based on both functional and expression analyses. First, we created a functional profile of AutRef84 by performing Gene Ontology (GO) enrichment analysis which encompassed three main areas: 1) neurogenesis/projection, 2) cell adhesion, and 3) ion channel activity. Second, we constructed an expression profile of AutRef84 by conducting DAVID analysis which found enrichment in brain regions critical for sensory information processing (olfactory bulb, occipital lobe), executive function (prefrontal cortex), and hormone secretion (pituitary). Disease specificity of this dual AutRef84 profile was demonstrated by comparative analysis with control, diabetes, and non-specific gene sets. We then screened the human genome with the dual AutRef84 profile to derive a set of 460 potential ASD candidate genes. Importantly, the power of our predictive gene map was demonstrated by capturing 18 existing ASD-associated genes which were not part of the AutRef84 input dataset. The remaining 442 genes are entirely novel putative ASD risk genes. Together, we used a composite ASD reference profile to generate a predictive map of novel ASD candidate genes which should be prioritized for future research.

Evolution of coauthorship in public health services and systems research.

CONTEXT: Public health services and systems research (PHSSR) focuses on the structure, organization, and legal basis of domestic public health activities and their effect on population health. An accurate description of the field is needed to empower funding agencies and other stakeholders to coordinate PHSSR activities and to foster the development of the field. The purpose of the study is to characterize the emerging community of researchers engaged in PHSSR. This study (1) describes dynamics of this growing community and (2) identifies research themes, subgroups within the field, and collaboration among groups. EVIDENCE ACQUISITION: Coauthorship network visualization of selected research publications in the MEDLINE bibliographic database between 1988 and May 2010. EVIDENCE SYNTHESIS: PHSSR has emerged gradually with noticeable growth after 1994 and after 2004. The network of PHSSR research has a core-periphery structure. The core includes highly collaborative researchers focusing on topics pertaining directly to PHSSR, such as workforce, quality improvement and performance, law, and information infrastructure. The periphery consists of groups publishing either on general health services research topics or on epidemiologic and clinical topics. CONCLUSIONS: Although a nucleus group of productive and engaged individuals participate in PHSSR, most also publish broadly on health services research and population health. This trend suggests that this emerging field cannot yet support a singular focus on PHSSR. Lack of funding sources and defined career paths likely contribute to this pattern. An overview of collaboration in PHSSR is an important step in advancing a coordinated research agenda and attracting sustainable funding streams for this field.

Evaluation of a prototype search and visualization system for exploring scientific communities.

Searches of bibliographic databases generate lists of articles but do little to reveal connections between authors, institutions, and grants. As a result, search results cannot be fully leveraged. To address this problem we developed Sciologer, a prototype search and visualization system. Sciologer presents the results of any PubMed query as an interactive network diagram of the above elements. We conducted a cognitive evaluation with six neuroscience and six obesity researchers. Researchers used the system effectively. They used geographic, color, and shape metaphors to describe community structure and made accurate inferences pertaining to a) collaboration among research groups; b) prominence of individual researchers; and c) differentiation of expertise. The tool confirmed certain beliefs, disconfirmed others, and extended their understanding of their own discipline. The majority indicated the system offered information of value beyond a traditional PubMed search and that they would use the tool if available.

Understanding interdisciplinary health sciences collaborations: a campus-wide survey of obesity experts.

This paper reports a campus-wide survey of obesity experts that allowed us to understand organizational factors and collaboration patterns affiliated with health sciences research. By combining Google and PubMed searches and the snowball sampling method, we identified and then surveyed 113 obesity experts on their collaborators, research interests, and affiliations with academic departments and research centers. The response rate was 61%. We describe the diversity in organizational affiliations, research interests, journals for disseminating results, and collaboration patterns among the respondents. We also analyze the challenges and research opportunities related to identifying experts and forging interdisciplinary health sciences collaborations. We conclude with possible success factors for sustained interdisciplinary collaborations.

Social network analysis of interdisciplinarity in obesity research.

Transdisciplinary research accelerates scientific progress. Despite the value of social network analysis to characterize interdepartmental collaboration, institutions have been slow to adopt the approach. We use the approach to characterize collaboration among obesity researchers at our institution, identifying cores of researchers engaged in frequent collaborations. Providing an objective view of research across an institution, social network analysis is a baseline for efforts to facilitate transdisciplinary collaboration.

Topological analysis of large-scale biomedical terminology structures.

OBJECTIVE: To characterize global structural features of large-scale biomedical terminologies using currently emerging statistical approaches. DESIGN: Given rapid growth of terminologies, this research was designed to address scalability. We selected 16 terminologies covering a variety of domains from the UMLS Metathesaurus, a collection of terminological systems. Each was modeled as a network in which nodes were atomic concepts and links were relationships asserted by the source vocabulary. For comparison against each terminology we created three random networks of equivalent size and density. MEASUREMENTS: Average node degree, node degree distribution, clustering coefficient, average path length. RESULTS: Eight of 16 terminologies exhibited the small-world characteristics of a short average path length and strong local clustering. An overlapping subset of nine exhibited a power law distribution in node degrees, indicative of a scale-free architecture. We attribute these features to specific design constraints. Constraints on node connectivity, common in more synthetic classification systems, localize the effects of changes and deletions. In contrast, small-world and scale-free features, common in comprehensive medical terminologies, promote flexible navigation and less restrictive organic-like growth. CONCLUSION: While thought of as synthetic, grid-like structures, some controlled terminologies are structurally indistinguishable from natural language networks. This paradoxical result suggests that terminology structure is shaped not only by formal logic-based semantics, but by rules analogous to those that govern social networks and biological systems. Graph theoretic modeling shows early promise as a framework for describing terminology structure. Deeper understanding of these techniques may inform the development of scalable terminologies and ontologies.