A/B Testing of User Enrollment Forms to Enhance Diversity in the Biomedical Workforce via the National Research Mentoring Network: User-Centered Design Case Study.

Background: The National Research Mentoring Network (NRMN) is a National Institutes of Health-funded program for diversifying the science, technology, engineering, math, and medicine research workforce through the provision of mentoring, networking, and professional development resources. The NRMN provides mentoring resources to members through its online platform-MyNRMN. Objective: MyNRMN helps members build a network of mentors. Our goal was to expand enrollment and mentoring connections, especially among those who have been historically underrepresented in biomedical training and the biomedical workforce. Methods: To improve the ease of enrollment, we implemented the split testing of iterations of our user interface for platform registration. To increase mentoring connections, we developed multiple features that facilitate connecting via different pathways. Results: Our improved user interface yielded significantly higher rates of completed registrations (P<.001). Our analysis showed improvement in completed enrollments that used the version 1 form when compared to those that used the legacy form (odds ratio 1.52, 95% CI 1.30-1.78). The version 2 form, with its simplified, 1-step process and fewer required fields, outperformed the legacy form (odds ratio 2.18, 95% CI 1.90-2.50). By improving the enrollment form, the rate of MyNRMN enrollment completion increased from 57.3% (784/1368) with the legacy form to 74.5% (2016/2706) with the version 2 form. Our newly developed features delivered an increase in connections between members. Conclusions: Our technical efforts expanded MyNRMN's membership base and increased connections between members. Other platform development teams can learn from these efforts to increase enrollment among underrepresented groups and foster continuing, successful engagement.

Diverse Mentoring Connections Across Institutional Boundaries in the Biomedical Sciences: Innovative Graph Database Analysis.

BACKGROUND: With an overarching goal of increasing diversity and inclusion in biomedical sciences, the National Research Mentoring Network (NRMN) developed a web-based national mentoring platform (MyNRMN) that seeks to connect mentors and mentees to support the persistence of underrepresented minorities in the biomedical sciences. As of May 15, 2024, the MyNRMN platform, which provides mentoring, networking, and professional development tools, has facilitated more than 12,100 unique mentoring connections between faculty, students, and researchers in the biomedical domain. OBJECTIVE: This study aimed to examine the large-scale mentoring connections facilitated by our web-based platform between students (mentees) and faculty (mentors) across institutional and geographic boundaries. Using an innovative graph database, we analyzed diverse mentoring connections between mentors and mentees across demographic characteristics in the biomedical sciences. METHODS: Through the MyNRMN platform, we observed profile data and analyzed mentoring connections made between students and faculty across institutional boundaries by race, ethnicity, gender, institution type, and educational attainment between July 1, 2016, and May 31, 2021. RESULTS: In total, there were 15,024 connections with 2222 mentees and 1652 mentors across 1625 institutions contributing data. Female mentees participated in the highest number of connections (3996/6108, 65%), whereas female mentors participated in 58% (5206/8916) of the connections. Black mentees made up 38% (2297/6108) of the connections, whereas White mentors participated in 56% (5036/8916) of the connections. Mentees were predominately from institutions classified as Research 1 (R1; doctoral universities-very high research activity) and historically Black colleges and universities (556/2222, 25% and 307/2222, 14%, respectively), whereas 31% (504/1652) of mentors were from R1 institutions. CONCLUSIONS: To date, the utility of mentoring connections across institutions throughout the United States and how mentors and mentees are connected is unknown. This study examined these connections and the diversity of these connections using an extensive web-based mentoring network.

Community perspectives on AI/ML and health equity: AIM-AHEAD nationwide stakeholder listening sessions.

Artificial intelligence and machine learning (AI/ML) tools have the potential to improve health equity. However, many historically underrepresented communities have not been engaged in AI/ML training, research, and infrastructure development. Therefore, AIM-AHEAD (Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity) seeks to increase participation and engagement of researchers and communities through mutually beneficial partnerships. The purpose of this paper is to summarize feedback from listening sessions conducted by the AIM-AHEAD Coordinating Center in February 2022, titled the "AIM-AHEAD Community Building Convention (ACBC)." A total of six listening sessions were held over three days. A total of 977 people registered with AIM-AHEAD to attend ACBC and 557 individuals attended the listening sessions across stakeholder groups. Facilitators led the conversation based on a series of guiding questions, and responses were captured through voice and chat via the Slido platform. A professional third-party provider transcribed the audio. Qualitative analysis included data from transcripts and chat logs. Thematic analysis was then used to identify common and unique themes across all transcripts. Six main themes arose from the sessions. Attendees felt that storytelling would be a powerful tool in communicating the impact of AI/ML in promoting health equity, trust building is vital and can be fostered through existing trusted relationships, and diverse communities should be involved every step of the way. Attendees shared a wealth of information that will guide AIM-AHEAD's future activities. The sessions highlighted the need for researchers to translate AI/ML concepts into vignettes that are digestible to the larger public, the importance of diversity, and how open-science platforms can be used to encourage multi-disciplinary collaboration. While the sessions confirmed some of the existing barriers in applying AI/ML for health equity, they also offered new insights that were captured in the six themes.

Genetic and Survey Data Improves Performance of Machine Learning Model for Long COVID.

Over 200 million SARS-CoV-2 patients have or will develop persistent symptoms (long COVID). Given this pressing research priority, the National COVID Cohort Collaborative (N3C) developed a machine learning model using only electronic health record data to identify potential patients with long COVID. We hypothesized that additional data from health surveys, mobile devices, and genotypes could improve prediction ability. In a cohort of SARS-CoV-2 infected individuals (n=17,755) in the All of Us program, we applied and expanded upon the N3C long COVID prediction model, testing machine learning infrastructures, assessing model performance, and identifying factors that contributed most to the prediction models. For the survey/mobile device information and genetic data, extreme gradient boosting and a convolutional neural network delivered the best performance for predicting long COVID, respectively. Combined survey, genetic, and mobile data increased specificity and the Area Under Curve the Receiver Operating Characteristic score versus the original N3C model.

BioEve Search: A Novel Framework to Facilitate Interactive Literature Search.

Background. Recent advances in computational and biological methods in last two decades have remarkably changed the scale of biomedical research and with it began the unprecedented growth in both the production of biomedical data and amount of published literature discussing it. An automated extraction system coupled with a cognitive search and navigation service over these document collections would not only save time and effort, but also pave the way to discover hitherto unknown information implicitly conveyed in the texts. Results. We developed a novel framework (named "BioEve") that seamlessly integrates Faceted Search (Information Retrieval) with Information Extraction module to provide an interactive search experience for the researchers in life sciences. It enables guided step-by-step search query refinement, by suggesting concepts and entities (like genes, drugs, and diseases) to quickly filter and modify search direction, and thereby facilitating an enriched paradigm where user can discover related concepts and keywords to search while information seeking. Conclusions. The BioEve Search framework makes it easier to enable scalable interactive search over large collection of textual articles and to discover knowledge hidden in thousands of biomedical literature articles with ease.