Biomedical Informatics & Data Science

1. Research

Research in informatics and data science, as well as its applications in clinical and basic sciences span various disease areas and technologies such as Natural Language Processing, AI/Machine Learning/polygenic risk scores, privacy technology, blockchain, social determinants of health, behavioral economics, etc. We started with a small group of faculty, and now total 29 primary appointments and 31 secondary appointments. BIDS primary faculty led 18 grant submissions as PI/MPI since January, and we participated in 13 others led by faculty from other departments (medicine, surgery, emergency medicine, psychiatry, to name a few). Our research staff (non-academic) grew to 20 professionals.

2. Service

We are re-engineering processes for clinical data access and analyses, as well as developing the computational infrastructure needed to employ AI and advanced analytics in a secure and privacy-protecting manner. In 2024, we will launch initiatives that allow researchers to compute with these data as well as data from other institutions participating in large clinical research networks. We are also partnering with the Cushing Library to determine which high-valued data sets would best serve the needs of Yale researchers (e.g., UK biobank, claims data, etc.), as well as for training in the use of popular data sets such those from the All of Us Research Program (we have launched a pilot recruitment site for the program right here in New Haven with help from the YCCI). Informatics and data science are critical components of precision medicine, and we will collaborate with colleagues from various units to ensure Yale is recognized as a leader in this area. Our goal is to ensure that informatics and data science are used not only to document health disparities, but are also used to reduce them.

3. Training & Education

We always keep in mind the university’s primary mission: disseminate knowledge to ensure that the products of research and service have a high impact in the real world, especially related to human health. We are slowly growing our trainee group, with 131 students across five programs and 11 postdoctoral fellows mentored by BIDS faculty. We also promote career development opportunities for staff and continued learning for all. BIDS faculty teach 17 graduate courses. We are designing new programs too.

I hope you will all work with us and follow our progress over time so we can look back in a couple of years and say we saw BIDS growing from a concept into a full-fledged, leading academic unit nationally and internationally. There is nothing more exciting than creating something new and seeing that it has made an impact in people’s lives. Thank you all so much for your warm welcome and contributions. Let’s do this together!

– Lucila Ohno-Machado, MD, MBA, PhD | Waldemar von Zedtwitz Professor of Medicine and Biomedical Informatics and Data Science | Deputy Dean for Biomedical Informatics | Chair, Section of Biomedical Informatics and Data Science

Recent papers published in Nature Digital Medicine, JAMA Network Open, the Journal of the American College of Cardiology: Advances and Circulation have been featured in the news. Produced by the Cardiovascular Data Science (CarDS) Lab in the Department of Medicine, Section of Cardiovascular Medicine, the research investigates wearable devices and AI as detection and prevention technologies for those at risk of cardiovascular disease. Study authors include Biomedical Informatics & Data Science faculty Cynthia Brandt and Rohan Khera, the latter of whom is director of the CarDS Lab.

As covered in Stat News, researchers developed a deep-learning algorithm to detect hidden cardiovascular disease based on electrocardiograms (ECG). Researchers proposed a training approach that accounts for noise and interference, which would otherwise degrade the quality of ECG readings taken in the real world. In August, the Hartford Courant reported that CarDS researchers were also able to develop a deep learning approach that uses ECG images to automatically screen for Left ventricular systolic dysfunction. These results are promising for wearable health devices and offer “significant potential” for the detection of disease in under-resourced settings, where hospital-grade equipment might not be readily accessible.

Meanwhile, tech magazine The Verge highlighted findings on the use of smartphones and tablets to track health goals. Using data from the National Cancer Institute, investigators analyzed smart device usage among 16,092 participants. They found that although older patients and male patients were at higher risk of cardiovascular disease, they were less likely to use a smart device to monitor their health. The research raises reminders about access and equity. Although the development of new health technologies continues to advance, affordability and technical literacy pose significant barriers. Victoria Song for The Verge noted, “The big question is whether any of it matters if the people who would most benefit are unlikely to use them.”

News Clips

Faculty affiliated with BIDS were also featured in news publications including Slate, MedPage Today and Yale School of Medicine news. Lee Schwamm, MD, Professor of Biomedical Informatics & Data Science; Dennis Shung, MD, MHS, PhD, Assistant Professor of Medicine (Digestive Diseases); and Janitza Montalvo-Ortiz, PhD, Assistant Professor of Psychiatry were interviewed for these articles.

Biomedical Informatics & Data Science

Montalvo-Ortiz Awarded NIDA Grant for Opioid Use Disorder Study

Oikonomou Awarded NIH Fellowship to Study Under-Diagnosed Heart Condition

Four of our scientists explain what NLP in biomedicine means for their research, and share perspectives on the opportunities of this fast-growing field.

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