Project Point of Contact: Nsini Umoh, Program Director / Matthew McAuliffe, Chief, SAS
Goals and Objectives:
- Increase transparency, scientific quality, and collaboration through improved access to Traumatic Brain Injury (TBI) data
- Enhance the system to better support AI ready data through the continued refinement of Common Data Elements (CDEs) by assigning Unified Medical Language System (UMLS) codes to each data element and its permissible values (i.e., value sets). The additional benefit of UMLS coding is that it supports the programmatic analysis of the data.
- Develop requirements and support the development to extend the Query GUI interface to efficiently use UMLS codes to develop cohorts
Significance: TBI is a major medical problem for both military and civilian populations. There are many critical gaps in our knowledge regarding how to diagnose and treat people who sustain a TBI. High priority gaps include the need for an objective diagnosis for mild TBI, biomarkers to track recovery or progression of injury, a biologically-based classification system, and comparative effectiveness research to determine which treatments are effective and for whom.
Description: TBI is a major cause of death, disability and a serious medical problem for both military and civilian populations. To better understand its impact and reduce TBI-related deaths, the Department of Defense (DoD) and the National Institute of Neurological Disorders and Stroke (NINDS) worked together to develop the Federal Interagency Traumatic Brain Injury Research program (FITBIR).
The FITBIR Informatics System (https://fitbir.nih.gov) is an extensible, scalable data science platform for TBI relevant data (medical imaging, clinical assessment, environmental and behavioral history, etc.) FITBIR was developed to share data across the entire TBI research field and to facilitate collaboration between laboratories, as well as interconnectivity with other informatics platforms. Said another way, FITBIR is a data science platform that supports end-to-end lifecycle support of research to accelerate and support NIH’s efforts with its data sharing goals (i.e., making data Findable Accessible, Interoperable, and Reusable (FAIR)).
Sharing data, methodologies, and associated tools, rather than summaries or interpretations of this information, can accelerate research progress by allowing re-analysis of data, as well as re-aggregation, integration, and rigorous comparison with other data, tools, and methods. This community-wide sharing requires common data definitions and standards, as well as comprehensive and coherent informatics approaches. The FITBIR system builds upon an effort to create common data elements (CDEs) for the study of TBI - which are essentially definitions and guidelines about the kinds of data that should be collected, and how to collect these data in clinical studies.
Data set(s) involved: FITBIR covers the full spectrum of age (children and adults), injury severity (concussion to death), and time (pre-hospital to chronic). The FITBIR system houses assessment data for over 80,000 research subjects and includes over 175,000 Clinical Imaging Datasets.
Today, FITBIR supports more than 140 studies and spans a hundred Principal Investigators (PIs), dozens of universities and research systems, the Department of Defense, and the National Institutes of Health.
Anticipated outcomes of the project:
- UMLS coding of CDEs
- Support query tool development used to identify TBI cohorts of interest
- Publication of results
Required skills of the DATA Scholar:
- Understand data science concepts - for example the use and application of Common Data Elements (CDEs)
- How to use query tools to support research
Expected/preferred length of DATA Scholar appointment: 1 year.
Expected/preferred time effort commitment of the DATA Scholar: Part time (50-99%)
Remote work preference: 100% remote allowable
ICO support: Work with TBI team members - developers and data scientists
Additional activities: Actively participate in NIH (e.g., NINDS, CIT) working groups as appropriate.
Career or professional development opportunities: Develop advanced and applied data science techniques.
Participate in NIH working groups as appropriate.
To apply to this or other DATA Scholar positions, please see instructions here: datascience.nih.gov/data-scholars.
