Bringing NLM to You

Guest post by Andrew Wiley, Video Producer, NLM Office of Communications and Public Liaison.

Before the COVID-19 pandemic, visitors from all over the world came to NLM for free, in-person, guided tours to learn about the largest biomedical library in the world. Visitors ranged from members of the public to students, educators, scientists, and nurses. They were introduced to many of NLM’s exciting research and information resources, such as the Visible Human Project — a library of digital images representing the complete anatomy of a man and a woman allowing visitors to discover a new perspective on the human body. Visitors could also explore the NLM Data Center, which houses the vital databases visitors know and love, such as PubMed,, MedlinePlus, and GenBank.

NLM is not your typical library. During tours, visitors could interact with the investigators in our NLM Intramural Research Program who are using computational biology and computational health science approaches to solve biological and clinical problems. Visitors could also descend into the underground stacks to see medical librarians scanning the world’s largest collection of scientific and medical literature. They could also view some of the world’s oldest and rarest medical books in NLM’s extensive historical collections — discovering just a few of the features that makes NLM so unique.

While the pandemic put a temporary stop to our ability to continue with physical tours of NLM, we know that visitors are eager for a virtual alternative. That’s why we created our new NLM Welcome Page.

This is where you can start your virtual tour and explore NLM’s offerings and resources. Here you can embark on a journey to explore some of what NLM has to offer through webpages that guide you from the world’s richest collections of historical material to the most cutting-edge data of the 21st century.

We want you to be able to experience NLM’s past, present, and future, and continue to see how NLM’s research and information services directly support scientific discovery, health care, and public health.

NLM is committed to serving scientists and society. What would you like to explore at NLM?

Photo of Andrew Wiley

Andrew Wiley is a video producer and writer for NLM’s Office of Communications and Public Liaison. Before joining NLM in 2008, Andrew produced local television in Frederick, Maryland and worked as a video journalist for The Frederick News-Post.

Video Transcript (below):

Hello, I’m Dr. Patti Brennan. I’m the Director of the National Library of Medicine.

As a nurse and an industrial engineer, I’ve spent my career making sure that information is available to help people make everyday health choices and to support biological and medical discoveries.

At the National Library of Medicine, we provide trusted information to scientists, to society, and to people living every day with healthcare challenges.

For over 200 years, the National Library of Medicine has been a partner in biological discovery, clinical care decision making, and health care choices in everyday living. We began humbly as a small collection of books in the 1800’s and now have grown to massive genomic databanks accessible worldwide every day by millions of people.

As one of the 27 Institutes and Centers here at the National Institutes of Health, we have three primary missions:

  • First, we have researchers that develop the tools that translate health data into health information and health action.
  • Second, we serve society by collecting the world’s biological and biomedical literature making it useful to scientists through our PubMed resource, and to everyday people through MedlinePlus.
  • Finally, we have a mission for outreach to make the National Library of Medicine’s resources accessible to everyone through our 7,000 points of presence around the United States. We make sure that the resources of the National Library of Medicine are available through public libraries, through hospital libraries, and in schools and clinics.

Making all of the resources of the National Library of Medicine available to the public requires a very large workforce. We have over 1,700 women and men working here. We have librarians, computer scientists, researchers, and biological scientists. We have individuals who understand clinical care, and who understand how to educate the public. We work together to make sure we can deliver—24 hours a day, 7 days a week—trusted health information.

Thank you for visiting us today. We hope you will join with us as we begin our third century bringing health information to scientists and society, accelerating biomedical discovery, improving health care, and ensuring health for all globally. Modernization Effort: Beta Releases Now Available

Guest post by Anna M. Fine, PharmD, MS, acting director of at the National Library of Medicine, National Institutes of Health.

Earlier this year, we provided an update on NLM’s efforts to modernize, the world’s largest publicly accessible database of privately and publicly funded clinical trials. NLM released a request for information, hosted public webinars, and adopted a user-centered design approach intended to help ensure that modernization is responsive to user needs.

These activities, together with input from the NLM Board of Regents Public Service Working Group on Modernization, supported the development of beta versions of a new website and components of the information submission system, also known as the Protocol Registration and Results System (PRS). The beta releases feature a modern look and feel and provide updated technology to support users.

NLM is pleased to announce the public availability of these beta releases, which present the first of many new features to come. Website and PRS Goals and Implementation
The goals of the beta releases of the website and components of the PRS are to:

  • Introduce users to the new technology platforms and evaluate their real-world performance,
  • Provide foundational features that will be expanded over time,
  • Provide PRS users with improved functionality to manage their record portfolios and workflows, and
  • Collect user input to inform future development activities.
Figure 1: The beta homepage is now available by clicking on the banner link on Home – or directly via

The beta sites will function in parallel to the current systems to allow for usability testing and iterative improvements and to enable features and functions to be added without disrupting the current user experience. Users will gradually be transitioned to the new sites, or site components, as they are completed. Communication about changes and their related timelines will occur early and often to allow an easy transition to the new user experience.

Highlights of the Beta PRS Release
Study sponsors and investigators play a critical role in providing the information that makes useful to data researchers, patients and their advocates, and the broader public. To better support the individuals who provide this information via the PRS, the modernization effort will make the submission of high-quality study information more intuitive, which in turn, will also benefit the end users of the data. These changes are intended to help PRS users meet existing reporting requirements; they do not alter those requirements.

The first beta PRS release will be available early 2022 and focuses on new workflow and portfolio management features such as customizable display and multiple download options to help data submitters save time. A recorded demonstration highlighting the features of the upcoming release is now available.

Highlights of the Beta Release
The modernization effort has implemented a responsive site design that better supports mobile device users and added information written in plain language. The current release focuses on basic features, with more features to be added over time based on user feedback.

The first beta release includes:

  • Redesigned home page and search feature,
  • Search results page with updated filter functionality,
  • New study record design with improved record navigation and study location information displayed using a web-based mapping application, and
  • Informational content presented in plain language such as Background and Learn About Studies webpages.

Again, this release is foundational, and more features will be added in the future. Importantly, we are developing advanced features to better meet the needs of data researchers and similar users, who use clinical trial information in their work.

Measuring Success, Next Steps, and Future Releases
User feedback received from website surveys, analytics, and continued engagement will help us assess the success of the first beta releases. Following validation of the technology’s performance and implementation of revisions made in response to user feedback, subsequent releases will address additional priorities identified by our user engagement efforts that are expected to help meet other patient and data researcher needs. Specific details of each component and release will be communicated to stakeholders as information becomes available.

For more information about activities and achievements during the modernization effort’s first two years, see the September 2021 NLM report summarizing the progress through August 2021, or view the October 2021 webinar for an update on the latest progress. To learn about related events and upcoming site enhancements and features, visit the Modernization webpage. We appreciate your support as we seek to improve the features and functionality of this critical NLM resource. If you would like to receive email updates, I encourage you to sign up for Hot Off the PRS.

Anna M. Fine has worked for the federal government for 16 years. She leads the program including the modernization initiative and research, policy, and outreach activities.

Turning Talent into Treasure

One of NLM’s greatest assets is its talented, creative workforce. Last year, NIH called on its 27 Institutes and Centers to step up to mount an effective response to COVID-19. Supported by Congress, NIH invested more than $2 billion to ensure rapid access to COVID-19 testing for everyone in the United States — funding research to accelerate access to vaccines and therapeutics and leveraging existing clinical trials and electronic health record data to characterize, monitor, and treat the long-term sequalae of COVID-19 infections.

How is NLM supporting NIH’s COVID-19 response? Well, not surprisingly, our literature and genomic repositories are key to inspiring new research and providing the reference annotated genomes used to evaluate the SARS-CoV-2 virus and help discern its variants. Our Network of the National Library of Medicine (NNLM) gives NLM a face in communities across the United States, providing trustable, community-specific health information and increasing community engagement in NIH research programs. Our researchers are developing new analytic tools to more efficiently interpret medical images and refine the taxonomy of viruses so the properties of related viruses can be better understood. All of these activities draw on the talents of our almost 1,700 staff and the extensive partnerships we have with collaborators within the government and across the country. But it’s our special knowledge of data science, library science, and informatics that is making it possible for NIH to set up many new research programs with systematic attention to data coordination, data reuse, and data integration.

I want to highlight the talents of people working diligently across NIH. When NIH receives congressional funding for new programs or innovative research, a lot of work happens behind the scenes before these funds are awarded to investigators. Program announcements are written, solicitations offered, proposals received and reviewed, and awards made. Each of these steps requires an enormous amount of human effort. NIH has staff engaged in all of these activities for our typical programs and standard research mechanisms. To date, NIH received almost $4.9 billion to fight COVID, which is about 8.8% of the NIH’s total budget of nearly $43 billion for fiscal year 2021. NIH efforts to address COVID required a legion of staff members to refocus their regular priorities to participate in this emergency response. The contributions of NLM staff in this effort were amazing, with nearly 50 people from NLM stepping up to help write funding announcements, participate in reviews, and/or managing the awards process.

In particular, I want to elevate the work of three of our NLM staff who have made significant contributions to this effort. Yanli Wang, PhD, is a program officer in our Division of Extramural Programs. Because of her expertise in data science and training in chemistry, Dr. Wang was detailed to the RADx Radical (RADx-rad) program. RADx-rad is supporting innovative approaches, including rapid detection devices and home-based testing technologies, that will address current gaps in COVID-19 testing and extend existing approaches to make them more usable, accessible, or accurate. Dr. Wang serves as the program officer for the Discoveries and Data Coordinating Center and is working to provide programmatic stewardship and make sure that data across all studies is collected in a systematic manner that fosters data integration and data reuse. A critical aspect of Dr. Wang’s work is fostering the uses of common data elements across the projects and over time.

Two NLM staff members support NIH’s Researching COVID to Enhance Recovery or RECOVER Initiative. RECOVER is studying the post-acute experiences of the estimated 10% to 30% of people who contract COVID-19 and continue to experience a range of symptoms. Amanda J. Wilson, Chief of NLM’s Office of Engagement and Training, is our representative to the RECOVER Initiative executive and coordinating committee. In this role she helps prepare the many funding announcements that stimulate research or reuse of clinical data to best understand this complex problem. Ms. Wilson leverages the extensive resource of the NNLM in support of community-based education and support of the COVID-19 crisis.

Another NLM staffer supporting the RECOVER Initiative is Paul Fontelo. In addition to his roles in training and research in NLM’s Intramural Research Program, Dr. Fontelo is a pathologist by training. He provides specialized expertise to the Autopsy Cohort Studies to identify tissue injury due to SARS-COV-2 infection, delivers technical direction to awardees, and approves certain deliverables and reports as required. He also participates in the application reviews of the Autopsy Cohort and the Mobile/Digital Health platform and is a member of the Post-Acute Sequelae of SARS-CoV-2 Executive Coordination Committee.

I’m grateful to these colleagues, and many more across NLM, who are going above and beyond their usual job responsibilities to help NIH step up to the challenges of the COVID-19 pandemic! Join me in thanking them for their efforts and using the talents of the NLM to create invaluable treasures for NIH!

Artificial Intelligence, Imaging, and the Promising Future of Image-Based Medicine

In mid-October I gave the NLM Research in Trustable, Transparent AI for Decision Support keynote speech to the 50th Institute of Electrical and Electronics Engineers (IEEE) Applied Imagery Pattern Recognition conference in Washington, D.C. (virtually, for me). The IEEE continues to advance new topics in applied image and visual understanding, and the focus this year was to explore artificial intelligence (AI) in medicine, health care, and neuroscience.

To prepare for my talk, I reviewed our extramural research portfolio so I could highlight current research on these topics. NLM’s brilliant investigators are using a range of machine learning and AI strategies to analyze diverse image types. Some of the work fosters biomedical discovery; other work is focused on creating novel decision support or quality improvement strategies for clinical care. As I did with the audience at IEEE, I’d like to introduce you to a few of these investigators and their projects.

Hagit Shatkay and her colleagues from the University of Delaware direct a project titled Incorporating Image-based Features into Biomedical Document Classification. This research aims to support and accelerate the search for biomedical literature by leveraging images within articles that are rich and essential indicators for relevance-based searches. This project will build robust tools for harvesting images from PDF articles and segment compound figures into individual image panels, identify and investigate features for biomedical image-representation and categorization of biomedical images, and create an effective representation of documents using text and images grounded in the integration of text-based and image-based classifiers.

Hailing from the University of Michigan, Jenna Wiens leads a project called Leveraging Clinical Time Series to Learn Optimal Treatment of Acute Dyspnea. Managing patients with acute dyspnea is a challenge, sometimes requiring minute-to-minute changes in care approaches. This team will develop a novel clinician-in-the-loop reinforcement learning (RL) framework that analyzes electronic health record (EHR) clinical time-series data to support physician decision-making. RL differs from the more traditional classification-based supervised learning approach to prediction; RL “learns” from evaluating multiple pathways to many different solution states. Wiens’ team will create a shareable, de-identified EHR time-series dataset of 35,000 patients with acute dyspnea and develop techniques for exploiting invariances (different approaches to the same outcome) in tasks involving clinical time-series data. Finally, the team will develop and evaluate an RL-based framework for learning optimal treatment policies and validating the learned treatment model prospectively.

Quynh Nguyen from the University of Maryland leads a project called Neighborhood Looking Glass: 360 Degree Automated Characterization of the Built Environment for Neighborhood Effects Research. Using geographic information systems and images to assemble a national collection of all road intersections and street segments in the United States, this team is developing informatics algorithms to capture neighborhood characteristics to assess the potential impact on health.

Corey Lester from the University of Michigan leads a multidisciplinary team using machine intelligence in a project titled Preventing Medication Dispensing Errors in Pharmacy Practice with Interpretable Machine Intelligence. Machine intelligence is a branch of AI distinguished by its reliance on deductive logic, and the ability to make continuous modifications based in part on its ability to detect patterns and trends in data. The team is designing interpretable machine intelligence to double-check dispensed medication images in real-time, evaluate changes in pharmacy staff trust, and determine the effect of interpretable machine intelligence on long-term pharmacy staff performance. More than 50,000 images are captured and put through an automated check process to predict the shape, color, and National Drug Code of the medication product. This use of interpretable machine intelligence methods in the context of medication dispensing is designed to provide pharmacists with confirmatory information about prescription accuracy in a way that reduces cognitive demand while promoting patient safety.

Alan McMillan from the University of Wisconsin-Madison and his team are examining how image interpretation can improve noisy data in a project called Can Machines be Trusted? Robustification of Deep Learning for Medical Imaging. Noisy data is information that cannot be understood and interpreted correctly by machines (such as unstructured text). While deep learning approaches (methods that automatically extract high-level features from input data to discern relationships) to image interpretation is gaining acceptance, these algorithms can fail when the images themselves include small errors arising from problems with the image capture or slight movements (e.g., chest excursion in the breathing of the patient). The project team will probe the limits of deep learning when presented with noisy data with the ultimate goal of making the deep learning algorithms more robust for clinical use.

In the work of Joshua Campbell’s team at Boston University, the images emerge at the end of the process to allow for visualization of large-scale datasets of single-cell data. The project, titled Integrative Clustering of Cells and Samples Using Multi-Modal Single-Cell Datauses a Bayesian hierarchical model developed by the team to perform bi-clustering of genes into modules and cells into subpopulations. The team is developing innovative models that cluster cells into subpopulations using multiple data types and cluster patients into subgroups using both single-cell data and patient-level characteristics. This approach offers improvements over discrete Bayesian hierarchical models for classification in that it will support multi-modal and multilevel clustering of data.

Several things struck me as I reviewed these research projects. The first was a sense of excitement over the engagement of so many smart young people at the intersection of analytics, biomedicine, and technology. The second was the variety of types of images considered within each project. While one study explores radiological images, another study examines how image data types vary from figures in journal papers to pictures of the built environment and images of workflows in a pharmacy. Two of these studies use AI techniques to analyze the impact of the physical environment to better understand its influence on patient health and safety, and one study uses images as a visualization tool to better support inference of large-scale biomedical research projects. Images appear at all points of the research process, and their effective use heralds an era of image-based medicine. Let’s see what lies ahead!

Leveraging the Value of Biomedical Informatics Across NIH

The American Medical Informatics Association (AMIA) 2021 Annual Symposium is coming to a close today, and I was honored to moderate NLM’s Annual Update Panel. This was an opportunity to talk about NLM’s contributions to NIH data science and tools, common data elements and clinical informatics. Over the last 40 years I’ve proudly served in many roles in AMIA and its predecessor organizations, including president, member of the Board of Directors, associate editor of the Journal of the American Medical Informatics Association, and numerous committee assignments; all of which fostered the advancement of health at the intersection of informatics, clinical, and biomedical knowledge. I’ve made many friends, been mentored by some of the greatest minds in the field, mentored others, and am grateful for the intellectual leadership and personal support provided by attendees at this meeting.

This year I am leading a completely new effort; for the first time in its 134-year history, NIH has multiple leaders across its Institutes and Centers who also are leaders in biomedical informatics. Together, with Michael F. Chiang, MD, Director of the National Eye Institute; Joshua Denny, MD, MS, CEO of NIH’s All of Us Program; Zhiyong Lu, PhD, FACMI, Senior Investigator in NLM’s National Center for Biotechnology Information; and Clement McDonald, MD, Chief, Health Data Standards Officer at NLM, I had the pleasure of leading a panel discussion about how informatics is accelerating efforts at NIH in support of biomedical discovery and the public health response to the COVID-19 pandemic.

NIH recognizes that the future of biomedical discovery rests, in part, on being able to leverage the knowledge embodied in clinical health records. For example:

  • As we learned throughout the pandemic, the insights we glean from clinical health records and from understanding the natural history of COVID-19 inform best practices for addressing its spread. More importantly, the ability to quickly and efficiently access clinical information provides an opportunity to titrate clinical trials in response to the ‘in-the-moment’ understanding of the course of an illness and clinical care for patients.
  • An improved understanding of the long-term course of COVID-19 and its clinical sequalae rests on being able to follow patients across time. Clarifying the impact of novel vaccines or clinical therapeutics would be enhanced by the ability to integrate participant information across any and every study in which the participant is represented.
  • NIH is engaged in exploring the value of contemporary and emerging informatics innovations, such as cloud-based reusable platforms and datasets, common data models, and the effective development and use of artificial intelligence and machine learning approaches to biomedical research and clinical practice.

Each of these requires effective deployment of informatics innovations into the research process.

AMIA’s Clinical Research Informatics community was foundational to the enhancement of the integration of biomedical informatics concepts into the research process. Much progress has been made to structure information for clinical and translational research and common data elements. Incremental progress must be praised, but to engage the operations of the world’s largest biomedical enterprise requires multiple touch points. Specifically, expanding the critical mass of leadership with expertise in biomedical informatics is essential for instituting enterprise-wide change.

Perhaps, as evidence of the importance of biomedical informatics in the research enterprise, NIH now has directors of three institutes and/or major operations who stand as leaders in the biomedical informatics community. The number of American College of Medical Informatics fellows among the NIH staff is expanding. Not only does this allow a ‘community of conversation,’ but embedding informatics expertise across NIH has accelerated the acceptance of, and the valuing of, biomedical informatics in the biomedical research enterprise. Together we are stimulating new biomedical informatics methods, processes, and the application of biomedical informatics innovation to science and health. I invite you to come join us!

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