Meet the NLM Investigators: Dr. Demner-Fushman Knows the Answers to Your Questions!

Meet my close colleague, Dr. Dina Demner-Fushman! This brilliant researcher is the face behind what many of you have already accessed on NLM’s websites. Many of you will agree with me when I say that having one PhD is extremely impressive–but would you believe she has TWO?! In addition to her master’s degree, Dr. Demner-Fushman has PhDs in immunology and computer science.

Dr. Demner-Fushman and her team use advanced artificial intelligence (AI), natural language processing, and data mining techniques to answer consumers’ questions about a variety of health topics. Did you know that it was Dr. Demner-Fushman’s research that led to the developmental stages of the indexing initiative that produced the current iteration of the MEDLINE resource? This work helps all of us navigate a plethora of NLM resources.

Check out the infographic below to learn more about the innovative, important research happening in Dr. Demner-Fushman’s lab.

Infographic titled: Biomedical Question Answering. The title area features a picture of Dr. Demner-Fushman along with her title and accreditations (MD, Phd): Investigator, Computational Health. The first column of the graphic explores her short and long-term goals  for her projects. The center column describes the processes she uses to achieve these goals, and the last column depicts a simple graphic illustrating a Q and A service.

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

It is a diverse, multicultural team. Some were even born after I got my first IT job checking computers at Hunter College for Y2K compliance. The team is united by the task of enabling computers to understand health-related information needs and the socioeconomic and professional status of people who come to NLM seeking information. It is a group of exceptionally dedicated and talented people. Our diverse backgrounds make us see all possible aspects of addressing the informational and emotional needs of our users. 

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

  • Be proactive: Seek information and take advantage of training opportunities.  
  • Be brave: Admit you don’t know or don’t understand something. Most people will try to help.  
  • Be bold: Reach out to people who you would like to work with or to discuss your ideas.  
  • Be honest.  
  • Be patient: Research implies working hard, sometimes without immediate results. Even if research is your passion and fun, sometimes you have to do things that you might not enjoy or you might fear but still have to do, like giving talks or writing paper.

What do you enjoy about working at NLM?  

The community of dedicated people across all divisions, the mission, and the intellectual freedom.  

Where are you planning to travel to this year?  

I was just in Dublin, Ireland, in May for the 60th meeting of the Association for Computational Linguistics and co-chaired the BioNLP workshop for the 15th time. I loved Dublin when I visited shortly before the pandemic. I enjoyed revisiting a place I loved and discovering new things to love.

What are you reading right now?  

In the Garden of Beasts by Erik Larson. It provides an amazing view of pre-World War II Germany and political relations. I hope some lessons have been learned! 

You’ve read her words, now hear them 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 Intramural Research Program (IRP), view job opportunities, and explore research highlights, I invite you to explore our recently redesigned NLM IRP webpage.

Transcript [Demner-Fushman]*: When people need information, what they really like is to ask a question and get a really good comprehensive answer, and to also know that the answer is true and correct.

When I started my independent clinician career, I had lots of questions, but I was sometimes not even sure if I was getting the right answer. “Question answering” is this system to understand the question, what the question is about, and why it is asked. When the answer is found, it’s usually not a single answer: It’s parts of the answer in different places. It’s multiple answers. So, all of that then needs to be condensed into one comprehensive answer with evidence of where the answer came from. So that’s the focus of my research.

On the surface, very similar questions asked by clinicians and by the public should be answered very differently. Different deep-learning systems are needed to find the answers to the same question asked by two different people.

The long-term goal is one entry point to all the NLM resources. It doesn’t matter who the person is and how they ask their question or look for information. We should be able to recognize what the person needs and provide it. There is no one—other than NLM—who is specifically dedicated to biomedical information retrieval and biomedical question answering. Although it seems industry is doing that kind of research as well, it is not their main focus, whereas we keep people focused on what really matters for health and advancing medicine.

*Transcript edited for clarity

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.

%d bloggers like this: