NIH All of Us Researcher Workbench – Data Browser

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The NIH All of Us Research Program is “part of an effort to advance individualized health care by enrolling one million or more participants to contribute their health data over many years”.

All of Us data is derived from various sources, including surveys, shared electronic health records, and much more. This collected data is housed in the All of Us Research Hub, which uses a tiered-data access model that includes a Public Tier dataset that “displays high-level summaries of the data available for research. Through the Data Browser, one can explore anonymized, aggregated participant data and summary statistics.”

As Memorial Sloan Kettering Cancer Center is listed as registered Institution with a Data Use and Registration Agreement (DURA) in place, MSK researchers can proceed to register for an account if they wish to gain access the curated datasets beyond the Public Tier dataset. 
Note: Authorized users of the All of Us data are expected to conduct research that follows and conforms to the All of Us Research Program data use policies.

The interactive, public Data Browser is a great place to learn about the type and quantity of data that All of Us collects so that interested researchers can start thinking about potential research questions that this data could help answer. Here’s a glimpse at what it looks like – from https://databrowser.researchallofus.org:

The Data Browser can be searched using keywords across all data types, or users can choose to click on the browsable tiles to explore a particular data type or source. 

From: https://databrowser.researchallofus.org/survey/social-determinants-of-health

For example, the Social Determinants of Health tile will lead users to more specific information, including a view of the survey questions themselves, each presented with a link to “See Answers” that leads to a breakdown of the aggregated participant answers.

To learn more about the NIH All of Us Researcher Workbench and to get an idea of how other researchers are already using this data, please check out the following resources:

…or Ask Us at the MSK Library!

Join us for “Adventures in Text Mining: Applications, Ethics, and Cancer Care”

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Promotional banner for Adventures in Text Mining eventJoin us for our webinar “Adventures in Text Mining: Applications, Ethics, and Cancer Care” on October 16 from 12:00 PM-1:00 PM Eastern Time.

What is Text Mining?
Text mining helps researchers sift through mountains of documents, clinical notes, and research papers to find important patterns and information quickly. Dr. Manika Lamba (Assistant Professor, School of Library and Information Studies, University of Oklahoma) will introduce the topic through the lens of her work in digital libraries and information organization.

Applications in Cancer Care
Dr. Anyi Li (Chief, Associate Attendings, Department of Medical Physics, Memorial Sloan Kettering) will explain how applying text mining technologies to clinical notes at MSK has automated radiation therapy processes, saving clinician time and allowing for risk event analysis and mitigation. He will address the ethical aspects of text mining in healthcare, including patient privacy and responsible data use.

Applications in the Published Literature
Text mining can allow researchers to analyze the vast volume of scientific literature. Dr. Zhiyong Lu (Senior Investigator, NIH/NLM, Deputy Director for Literature Search, NCBI) will showcase his work mining the literature in PubMed, which led to tools including the Best Match algorithm and LitCovid. 

Register now. All registrants will receive a link to the event recording, whether or not they can attend synchronously.

About the speakers:

Dr. Manika Lamba is an Assistant Professor at the School of Library and Information Studies, University of Oklahoma. Previously, she served as a Postdoctoral Research Associate at the HathiTrust Research Center, University of Illinois. Her research broadly falls under computational social science and science of science. She primarily focuses on using computational methods, such as text mining and machine learning, to provide better solutions for information retrieval and organization of digital libraries.

Dr. Anyi Li, Associate Attending Physicist and Chief of Computer Service at the Department of Medical Physics at MSK, leads a talented team comprising mathematicians, physicists, engineers, and data scientists. Together, they collaborate with the Division of Clinical Physics and the Department of Radiation Oncology to harness artificial intelligence, operational research algorithms, and big data. Their objective is to optimize radiation therapy plans, enhance the efficiency of the radiation treatment process from start to finish, develop a data platform for clinical decision support, and improve patient safety by managing accumulated radiation doses. They utilize the latest language models to analyze clinical event timelines and construct workflow knowledge graphs, which improve the radiation therapy workflow and provide valuable insights to the clinical team. With a background as a theoretical nuclear physicist and research scientist tackling NP-hard (nondeterministic polynomial time) problems, Dr. Li transitioned into big data engineering and AI, bringing experience from positions at Yahoo and IBM Watson Health.

Dr. Zhiyong Lu is a tenured Senior Investigator at the NIH/NLM IPR, leading research in biomedical text and image processing, information retrieval, and AI/machine learning. In his role as Deputy Director for Literature Search at NCBI, Dr. Lu oversees the overall R&D efforts to improve literature search and information access in resources like PubMed and LitCovid, which are used by millions worldwide each day. Additionally, Dr. Lu is Adjunct Professor of Computer Science at the University of Illinois Urbana-Champaign (UIUC). With over 400 peer-reviewed publications, Dr. Lu is a highly cited author, and a Fellow of the American College of Medical Informatics (ACMI) and the International Academy of Health Sciences Informatics (IAHSI).

25 Years of MedlinePlus

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The U.S. National Library of Medicine (NLM)’s consumer health information online resource, MedlinePlus, just turned 25 years old! For a historical look back – see:

25 Years of Consumer Health Information: MedlinePlus Celebrates Its Silver Anniversary – NLM Musings from the Mezzanine (nih.gov)

Soon after NLM made the PubMed database (a free index to the biomedical and life sciences literature aimed primarily at health care professionals and researchers) available in 1996, NLM realized that the need for accessible and authoritative health information extended beyond health professionals to the general public.

And so MedlinePlus.gov came online starting in Fall 1998 and has continued to grow and evolve ever since.

Some noteworthy MdlinePlus enhancements over the years have been the inclusion of quality health information in Spanish, information about herbs and supplements, drug information summaries, medical test summaries, information about genes and genetics conditions, healthy recipes, and over 1000 health topics.

Most recently, in 2020, another NLM resource, Genetics Home Reference (GHR), was incorporated into MedlinePlus in the form of the MedlinePlus Genetics module that includes easy-to-understand “Help Me Understand Genetics” pages intended for patients.

Also worthy of highlighting have been NLM’s efforts to expand the reach of this valuable consumer health information by creating MedlinePlus Connect, “a free service that links electronic health records (EHRs), patient portals, and other health IT systems to relevant, authoritative, and up-to-date health information from NLM’s MedlinePlus health information resource and other NIH websites.” To understand how MedlinePlus Connect works, click here. The National Cancer Institute has also collaborated “to expand the scope of content in MedlinePlus Connect”.

Learn more:

Burgess S, Dennis S, Lanka S, Miller N, Potvin J. MedlinePlus Connect: Linking Health IT Systems to Consumer Health Information. IT Prof. 2012 May;14(3):22-28. doi: 10.1109/mitp.2012.19. PMID: 23066351; PMCID: PMC3469315.

Questions? Ask Us at the MSK Library!