The Next Normal: Supporting Biomedical Discovery, Clinical Practice, and Self-Care

As we start year three of the COVID-19 pandemic, it’s time for NLM to take stock of the parts of our past that will support the next normal and what we might need to change as we continue to fulfill our mission to acquire, collect, preserve, and disseminate biomedical literature to the world.

Today, I invite you to join me in considering the assumptions and presumptions we made about how scientists, clinicians, and patients are using critical NLM resources and how we might need to update those assumptions to meet future needs. I will give you a hint… it’s not all bad—in fact, I find it quite exciting!

Let’s highlight some of our assumptions about how people are using our services, at least from my perspective. We anticipated the need for access to medical literature across the Network of the National Library of Medicine and created DOCLINE, an interlibrary loan request routing system that quickly and efficiently links participating libraries’ journal holdings. We also anticipated that we were preparing the literature and our genomic databases for humans to read and peruse. Now we’re finding that more than half of the accesses to NLM resources are generated and driven by computers through application programming interfaces. Even our MedlinePlus resource for patients now connects tailored electronic responses through MedlinePlus Connect to computer-generated queries originating in electronic health records.

Perhaps, and most importantly, we realize that while sometimes the information we present is actually read by a living person, other times the information we provide—for example, about clinical trials (ClinicalTrials.gov) or genotype and phenotype data (dbGaP)—is actually processed by computers! Increasingly, we provide direct access to the raw, machine-readable versions of our resources so those versions can be entered into specialized analysis programs, which allow natural-language processing programs to find studies with similar findings or machine-learning models to determine the similarities between two gene sequences. For example, NLM makes it possible for advocacy groups to download study information from all ClinicalTrials.gov records so anyone can use their own programs to point out trials that may be of interest to their constituents or to compare summaries of research results for related studies.

Machine learning and artificial intelligence have progressed to the point that they perform reasonably well in connecting similar articles—to this end, our LitCovid open-resource literature hub has served as an electronic companion to the human curation of coronavirus literature. NLM’s LitCovid is more efficient and has a sophisticated search function to create pathways that are more relevant and are more likely to curate articles that fulfill the needs of our users. Most importantly, innovations such as LitCovid help our users manage the vast and ever-growing collection of biomedical literature, now numbering more than 34 million citations in NLM’s PubMed, the most heavily used biomedical literature citation database.

Partnerships are a critical asset to bring biomedical knowledge into the hands (and eyes) of those who need it. Over the last decade, NLM moved toward a new model for managing citation data in PubMed. We released the PubMed Data Management system that allows publishers to quickly update or correct nearly all elements of their citations and that accelerates the delivery of correct and complete citation data to PubMed users.

As part of the MEDLINE 2022 Initiative, NLM transitioned to automated Medical Subject Headings (MeSH) indexing of MEDLINE citations in PubMed. Automated MeSH indexing significantly decreases the time for indexed citations to appear in PubMed without sacrificing the quality MEDLINE is known to provide. Our human indexers can focus their expertise on curation efforts to validate assigned MeSH terms, thereby continuously improving the automated indexing algorithm and enhancing discoverability of gene and chemical information in the future.

We’re already preparing for the next normal—what do you think it will be like?

I envision making our vast resources increasingly available to those who need them and forging stronger partnerships that improve users’ ability to acquire and understand knowledge. Imagine a service, designed and run by patients, that could pull and synthesize the latest information about a disease, recommendations for managing a clinical issue, or help a young investigator better pinpoint areas ripe for new interrogation! The next normal will make the best use of human judgment and creativity by selecting and organizing relevant data to create a story that forms the foundation of new inquiry or the basis of new clinical care. Come along and help us co-create the next normal!

Meet the NLM Investigators: For Sameer Antani, PhD, Seeing is More Than Meets the Eye

It’s time for another round of introductions! Many of you may already know Sameer Antani, PhD—one of NLM’s most decorated and prestigious investigators—from his many awards and accolades. In March 2022, he was inducted into the American Institute for Medical and Biological Engineering’s College of Fellows, an impressive group that represents the top two percent of medical and biological engineers. This distinction is one of the highest honors that can be bestowed upon a medical and biological engineer. Can you tell we are proud of him?!  

We selected Dr. Antani to join our NLM family after a nationwide, competitive search, and his genius was readily apparent from the start. Dr. Antani’s career spans over two decades, during which he developed an innovative research portfolio focused on machine learning and artificial intelligence (AI). His lab at NLM focuses on using these tools to analyze enormous sets of biomedical data. Through this analysis, AI technology can “learn” to detect disease and assist health care professionals provide more efficient diagnoses. Examples of Dr. Antani’s work can be found in mobile radiology vehicles, which allow professionals to take chest X-rays and screen for HIV and tuberculosis using software containing algorithms developed in his lab. Check out the infographic below to learn more about the exciting research happening in Dr. Antani’s lab.

Infographic titled: Seeing is more than meets the eye. Under the title the investigator's name, title and division are listed as: Sameer Antani, PhD, Investigator, Computational Health. 

The first column of the infographic is titled: Projects. Two bullets are listed in the first column. The first bullet reads: Discovering the impact of data on automated AI and machine learning (AI/ML) processes on diagnostics. The second bullet says: Improving AI/ML algorithm decisions to be consistent, reproducible, portable, explainable, unbiased, and representative of severity.

The second column is titled: Process. The first bullet in this column reads: Using images and videos alongside AIML technology to identify and diagnose:
Cancers: Cervical, Oral, Skin (Kaposi Sarcoma)
Cardiomyopathy 
Cardiopulmonary diseases. 
The second bullet reads: Analyzing a variety of image types, including:
Computerized Tomography (CT), Magnetic Resonance Imaging (MRI), X-ray, ultrasound, photos, videos, microscopy. 

The third and final column in the infographic is titled: What It Looks Like. In this column there are four images of chest x-rays illustrating the detection of HIV and TB.

Now, in his own words, learn more about what makes Dr. Antani’s work so important!

What makes your team unique? Tell us more about the people working in your lab.   

The postdoctoral research fellows, long-term staff scientists, and research scientists on my team explore challenging computational health topics while simultaneously advancing topics in machine learning for medical imaging. Dr. Ghada Zamzmi, Dr. Peng Guo, and Dr. Feng Yang bring expertise and drive to our lab. The scientists on my team, Dr. Zhiyun (Jaylene) Xue and Dr. Sivarama Krishnan Rajaraman, add over two decades of combined research and mentoring experience.  

What do you enjoy about working at NLM?  

There are many positives about working at NLM. At the top of the list is the encouragement and support to explore cutting-edge problems in medical informatics, data science, and machine intelligence, among other initiatives. 

What is your advice for young scientists or people interested in pursuing a career in research?  

I urge young scientists to recognize the power of multidisciplinary teams. I would also urge them to develop skills to clearly communicate their goals and research interests with colleagues who might be from a different domain so they can effectively collaborate and arrive at mutually beneficial results. 

Where is your favorite place to travel?

I like to travel to places that exhibit the natural wonders of our planet. I hope to visit all our national parks someday. 

When you’re not in the lab, what do you enjoy doing?

I am studying and exploring different aspects of music structure.

You’ve read his words, and now you can hear him for yourself! Follow our NLM YouTube page for more exciting content from the NLM staff that make it all possible. If you’d like to learn more about our IRP program, view job opportunities, and explore research highlights, I invite you to explore our recently redesigned NLM IRP webpage.

YouTube: Sameer Antani and Artificial Intelligence

Transcript: [Antani]: I went to school for computer engineering in India. I’ve worked with image processing, computer vision, pattern recognition, machine learning. So my world was filled with developing algorithms that could extract interesting objects from images and videos. Pattern recognition is a family of techniques that looks for particular pixel characteristics or voxel characteristics inside an image and learns to recognize those objects. Deep learning is a way of capturing the knowledge inside an image and encapsulating it, and then researchers like me spend time advancing newer deep-learning networks that look more broadly into an image, recognizing these objects—recognizing organs, in my case, and diseases—and converting those visuals into numerical risk predictors that could be used by clinicians.

So my research is currently in three very different areas. One area looks at cervical cancer. A machine could look at the images and be a very solid predictor of the risk to the woman of developing cervical precancer, encouraging early treatment. Another area I work with [is] sickle cell disease. One of the risk factors in sickle cell disease is cardiac myopathy or cardiac muscle disease, which leads to stroke and perhaps even death. Looking at cardiac echo videos and using AI to be a solid predictor, along with other blood lab tests, improves the chances of survival.

A third area that I’m interested in is understanding the expression of tuberculosis [TB] in chest X-rays, particularly for children and those that are HIV-positive. The expression of disease in that subpopulation is very different from adults with TB who are not HIV positive. Every clinician has seen a certain number of patients in their clinical training. They perhaps have spent more time at hospitals or clinical centers, been exposed to a certain population, and they become very adept at that population. Machines, on the other hand, could be trained on data that is free of bias, from different parts of the world, different ethnicities, different age groups, so that there’s an improved caregiving and therefore, a better expectation on treatment and care.

Note: Transcript was modified for clarity.

A New Frontier: The Impact of a 1959 Board Meeting

Guest blog by Ken Koyle, MA, Deputy Chief of the History of Medicine Division (HMD) at the NIH National Library of Medicine. This post celebrates the important work performed by our archival professionals and the archival collections held by the library, from which the source material was drawn, as NLM celebrates International Archives Week #IAW2022.

In November 1959, when construction of NLM’s current building at NIH was still underway and digital computing was in its infancy, the NLM Board of Regents convened on the third floor of the Old Red Brick building for a demonstration of the indexing process. When Board Chairman Michael E. DeBakey, MD, asked if computer technology could be used in indexing, NLM Director Col. Frank B. Rogers, MD, was ready with an answer. Dr. Rogers, clearly interested in the emerging technology of automated data processing (ADP), described an article by Robert S. Ledley, DDS, in that month’s issue of Science and noted that Dr. Ledley was already contracted with NLM to report on using computers in indexing.

Black-and-white photo of Dr. Rogers leaning on a stack of books with bookshelves in background.
Dr. Frank Rogers at NLM, 1962.

Dr. Rogers was instrumental in NLM’s first explorations of automated processes and had a clear vision of the potential of electronic computing, including how it could improve efficiency at NLM, but his optimism was tempered by prescient realism. Dr. Rogers recognized—and conveyed to the Board—that the potential benefits of ADP would require a commensurate investment of staff time and labor. “We should not forget that ‘automatically’ means ‘because we told it to do so beforehand,’ and this in itself may turn out to be quite a trick.” Dr. Rogers made it clear that the computer age would bring a change in work, but not necessarily a reduction in work. “Remarkable as the capacity of the computer may be for sustaining a long sequence of operations, it is nevertheless ultimately only the end-phase of that still longer sequence which must include as a first phase the human labor of input.”

Acknowledging the upfront labor investment in ADP was only part of Dr. Rogers’ insight. He also explained that the human work was not only substantial and necessary, but also incredibly complex: “The instructions [for a computer] are a thousand times more detailed, for the simplest task, than those required to be given to the . . . clerk.” Unleashing computers’ potential would require staff to think in new ways, conceive new methods of organizing data, and embark on a new journey of continuous learning and professional development.

Black-and-white photo of members of the NLM Board of Regents posing for a photo. Four members sit behind a table stacked with papers. 13 members stand in the background. Dr. Rogers is featured on the far right.
Dr. Frank Rogers (far right) with the NLM Board of Regents meeting in the “Old Red Brick,” 1957.

Along with the challenges of training staff to work with ADP equipment came the interminable problem of cost. Much as today’s public institutions are grappling with the costs of cloud computing, digitization, and increasing storage requirements, Dr. Rogers had to balance the potential benefits with the considerable costs of computer equipment. The type of computer necessary to realize Dr. Rogers’ vision would cost about $1.5 million in 1960—98% of NLM’s total budget of $1,566,000.

Undeterred, Dr. Rogers found an answer to the funding problem by collaborating with another agency that would benefit from the increased processing speed of scientific literature that the envisioned system could provide: the National Heart Institute. They provided the initial funding, NLM did the legwork, and in 1963, the new MEDLARS computer went into service. Dr. Rogers had realized his vision of bringing automated indexing to NLM. As Surgeon General Luther Terry said at the Board meeting in April 1961, “If any institution ever stood on the borderland of a new frontier it is the National Library of Medicine.”

Computer operators working with the Honeywell 800 mainframe computer, originally acquired by NLM in the 1960s.

Dr. Rogers was very clear about the issues of cost, labor, and expectations in his 1960 presentation to the Board, including his overarching concern about balancing NLM’s core mission with these potential new directions:

[The] purpose of the Library is not to operate a particular machine system, however great an acrobatic achievement that might be in itself. It is not to publish and distribute a particular index in a particular way, however ingenious and successful that operation may be deemed to be. It is not even just to be a good library, however great and distinguished that library may be. It is rather, by virtue of being a library, to use every available bibliothecal means to promote awareness of and access to the subject content of recorded medical knowledge, to the end that the science of medicine will advance and prosper.

More than 60 years later, NLM still holds fast to that purpose. As stated in our statutory mission and reiterated in our current strategic plan, we are here “to assist the advancement of medical and related sciences and to aid in the dissemination and exchange of scientific and other information important to the progress of medicine and to the public health.” Our continued pioneering work in data science is just one way we accomplish that mission.

Mr. Koyle joined HMD in the NLM Division of Library Operations in 2012. Before joining NLM, Ken served as a medical evacuation helicopter pilot and a historian in the U.S. Army. He is the co-editor with Jeffrey Reznick of Images of America: U.S. National Library of Medicine, a collaborative work with HMD staff.

We Can’t Go It Alone!

In February, I received the Miles Conrad Award from the National Information Standards Organization (NISO). NISO espouses a wonderful vision: “. . . a world where all can benefit from the unfettered exchange of information.” As the Director of the National Library of Medicine (NLM), this is music to my ears.

Standards are essential to NLM’s mission! Standards bring structure to information, assure common understanding, and make the products of scientific efforts—including literature and data—easier to discover. NLM’s efforts are devoted to the creation, dissemination, and use of terminology and messaging standards. These efforts include attaching indexing terms to citations in PubMed, our biomedical literature database housing over 34 million citations; using reference models to describe genome sequences; and serving as the HHS repository for the clinical terminologies needed to support health care delivery. NLM improves health and accelerates biomedical discovery by advancing the availability and use of standards. Standards are dynamic tools that must capture the context of biomedicine and health care at a given moment yet reflect the scientific development and changes in community vernacular.

By their very nature, standards create consensus across two or more parties on how to properly name, structure, or label phenomena. No single entity can create a standard all by itself! Standards are effective because they shape the conversation between and among entities, achieving a common goal by drawing on a common representation.

NLM alone cannot create, promulgate, or enforce standards. We work in partnership with professional societies, standards development organizations, and other federal entities, including the Office of the National Coordinator for Health Information Technology, to foster interoperability of clinical data. We support the development and distribution of SNOMED CT (the Systematized Nomenclature of Medicine – Clinical Terms) and the specific extension of SNOMED in the United States. We developed the MeSH (Medical Subject Headings) thesaurus, a controlled vocabulary used to index articles in PubMed. We also support the development and distribution of LOINC (the Logical Observation Identifiers Names and Codes), a common language—that is, a set of identifiers names and codes—used to identify health measurements, observations, and documents. Finally, we maintain RxNorm, a normalized naming system for generic and branded drugs and their uses, to support message exchanges across pharmacy management and drug interaction software.

Partnerships help us create and deploy standard ways to make scientific literature discoverable and accessible. To this end, we were instrumental in the adoption of NISO’s JATS (Journal Article Tag Suite), an XML format for describing the content of published articles, which we encourage journals to use when submitting citations to PubMed so users can efficiently search the literature and articles as they are described. MeSH RDF (Resource Description Framework) is a linked data representation of the MeSH vocabulary on the web, and the BIBFRAME (Bibliographic Framework) Initiative—a data exchange format initiated by the Library of Congress—adds MeSH RDF URIs (Uniform Resource Identifiers) to link data that will support complete bibliographic descriptions and foster resource sharing across the web and through the networked world.

Standards provide the resources necessary to understand complex phenomena and share scientific insights. Leveraging partnerships in order to develop and deploy these standards both allows efficiencies and produces a more connected, interoperable, understandable world of knowledge. Given the speed at which biomedical knowledge is growing, leveraging these partnerships assures that the institutions charged with acquiring and disseminating all the knowledge relevant to biomedicine and health can successfully and effectively meet their missions.

Please Join Us in Honoring Milton Corn, MD

This blog post is based on remarks given at the May 17 Milton Corn Memorial Concert.

Yesterday, I was honored to join in a beautiful celebration for the life of Dr. Milton Corn, an amazing man who I regarded as my adviser, colleague, and—most importantly—my friend. I would like to thank his wife Gilan and all of Milt’s friends and family for creating that wonderful moment of togetherness. Many of us knew Milt when he was Dean of the School of Medicine at Georgetown University or in his role with the National Library of Medicine, and I suspect that some even knew him as a bon vivant around town!

While I’m sure many of you can remember the moment you met Milt, I actually can’t—in my mind, it seems like he was an ever-present professional of the big data and scientific technology community! As a newly minted PhD in the late 1980s/early 1990s, I remember Milt as eminent in our field… and that was 30 years ago! I got to know Milt as part of the medical informatics community that was just emerging as a research powerhouse. Milt was a mentor to me; he reached into the visions I had for—and breathed life into—the ways technology could support patient engagement. He was always supportive, but he was also a hard questioner who wanted to know the value of the community’s investment.

Milt brought so many gifts to the field of biomedical informatics. He brought his wisdom as a physician executive to a fledgling field, applying his gentle but direct guidance to inspire research in the domain. Milt also funded my research; I remember a phone call one August afternoon over 20 years ago when Milt said, “Do you still need money for this project? Because we have some end-of-year money for you, and it’s available if you want to use it,” which of course let us advance our original ComputerLink project.

Interestingly enough, I actually know very little about Milt’s role at NLM, although I know a lot about his contributions! He joined our beloved NLM in 1990 during the first decade of applying computer technology to health care, in support of Don Lindberg’s visionary leadership. Milt served as NLM’s ambassador to the broader academic and research community as both their instigator and a supporter of many novel research ideas. Milt was in love with ideas, but he never let that love cloud his judgment or interfere with his expectation that emerging fields needed good science. He was as enchanted with a novel approach to genetic analysis as he was with securing proposals to write important books that detailed the history of medicine.

Milt became a colleague, a trusted advisor, and someone I could talk with about biomedical informatics. We could laugh about the field while enjoying its growth. Later, Milt became my friend. We shared family stories, our love for our children, and the challenges we faced with them. I loved his humor—he had the best sardonic laugh in the world. And then, surprise of all surprises, Milt became my employee, which had nothing to do with his actions, but with my actions! I remember being very mindful of Milt during my first NIH interview, where one of the committee members asked what it was going to be like for me, and I said I’m now going to be the boss of someone who I felt that I have learned from my whole career… it’s going to be fabulous!

Not that it wasn’t daunting; for 25 years, my career success depended on Milt! And he was wise: on my first day on the job, Milt stopped by with a little gift—a bag of peanut M&Ms! What a way to level the playing field. Sometime during those first few weeks, Milt came to my office and said, “Anytime you need my desk for someone else, you just let me know, and I’ll go home.” Every year he would say that sentence, and every year I thought not yet, I need you here. I couldn’t be without Milt, the magic maker.

After working more closely with Milt, I realized his judgment, discernment, and incredibly keen sense of what was a good investment—and, more importantly, what wasn’t—were critical to how NLM functioned. Later in our time at NLM, we needed a single scientific director to unify our intramural programs, and Milt took this responsibility on. Adding the title of Acting Scientific Director to his already stretched ambit, Milt aligned our two very strong intramural research groups: one addressing computational biology, and the other, clinical health informatics. He guided these two very disparate groups of investigators into a single structure… not totally unified, but respectful of each other and clearly willing to meet halfway across the bridge.

I turned to Milt many times as counselor to my position. Navigating the federal waters as director of a venerable institute like the NIH National Library of Medicine was a challenge—even for someone who thought herself quite sophisticated in dealing with complex organizations. Periodically, I’d walk over to Milt’s office, settle into one of his nice leather chairs, and lay out whatever issue I was confronting or a personality that perplexed me. Through a question or a brief comment, he led me to solutions, insights, and confidence, but none more so than the day he said, “Your job is important, and you deserve to have fun—so make sure that you do that!” I am brimming with tears as I remember how his strength made me strong!

In October of 2020, Milt told me that the pandemic was good for him. What an odd statement, I thought. However, he revealed that our maximum telework posture, with everyone working from home, eliminated the need for him to make the long commute from Virginia to Bethesda. Working from home made it possible for him to continue to engage. And engage he did! He remained a mentor all the way up until his very last weeks at the National Library of Medicine. I remember the night he called me and said, “I don’t think I can come back to work anymore,” but he reminded me, “You can call me if you need me.” I took his generous offer to heart and took it up as often as I could.

Above all, Milt was important to me, to the National Library of Medicine, and to the entire scientific and clinical world. Thank you.

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