Welcome to the CBIIT Speaker Series Wiki
The well-known phenomenon of "information explosion" has impacted virtually all areas of human enterprise, and healthcare has become no exception. While one might quibble whether more information is actually being created, there is no disagreement that vastly more information is being electronically captured and stored. Latent within the proliferation of such machine readable archives of information lays previously impractical metrics, capabilities for linkages and association, and ultimately new knowledge. The over-used moniker of "big data" is applied to the rise of vast, potentially-federated data sources, analytic methods for their interpretation, and emergent findings. Despite this non-precision, most observers agree that there is something new and different emergent in the opportunistic mining of disparate data on an unprecedented scale.
Examples of impressive inferences from big data abound in finance, marketing, education, social sciences, and economics. More focused, "big science" opportunities are self-evident in astronomy, physics, and arguably the discovery of the Higgs Boson (which really was inferred from perturbations observed across Exabytes of experimental particle-accelerator data). In biology and medicine the sweet spot has historically been in the human genome, where genotype-phenotype associations emerge from "genome-wide association studies" done at massive scale — more so in the present era of whole-genome sequencing.
The promise of best-evidence discovery, comparative effectiveness research, new outcomes analyses, adverse event discovery, and improved clinical care in general that might emerge from big-data methods applied to large, federated, clinical data repositories is intriguing. There is "gold in them hills," and the potential benefits of well-conducted data mining must not be lightly dismissed.
However, caution must dominate an otherwise unfettered analyses of clinical information, as the consequences of skewed, biased, spurious, or otherwise "wrong" answers can have serious adverse impact. While most of us are quite content to have a target answer appear "on the page" of a Google search result, somehow having the right answer "on the list" but not chosen for healthcare interventions may be interpreted as malpractice in some litigious countries — not to mention likely sub-optimal outcomes for a patient. Clinical decision support resources may recommend a spectrum of options to a clinician — who presumably has the responsibility of synthesizing such advice and selecting the optimal path, though few would argue that the amount of information and the complexity of their interactions have long ago exceeded the unaided human capacity for cognition, reliable processing, or well-balanced interpretation.
The importance of comparable and consistently represented clinical information, either at entry or through normalization to a canonical form, must remain as a necessary step before big-data methods can be meaningfully or safely applied to clinical data repositories.
The need for decision support systems in radiology is growing given the dramatic increase in imaging utilization, intensity and workload. Dr. Summers' laboratory at NIH focuses on the application of advanced image processing and machine learning techniques to provide decision support for radiology image interpretation. As a body radiologist and CT subspecialist, Dr. Summers has chosen to focus his research on the development of decision support for thoracoabdominal CT image interpretation.
In this talk, Dr. Summers will discuss his laboratory's approach to full automation of body CT interpretation. In the last three years, his laboratory has made substantial progress towards this goal. Topics will include fully-automated detection and segmentation of major body organs and their lesions, including spine and spine lesions and lymphadenopathy. Validation results will be presented. Dr. Summers will describe potential unrecognized benefits of fully-automated quantitation on routine body CT scans without the need for additional radiation exposure. He will also discuss the impact of advances in deep learning to radiology image analysis.
PhenoDB is a web-based tool developed for the collection, storage and analysis of phenotype data, as well as interpretation of exome and genome data in the context of phenotype data. It has it own taxonomy and links to OMIM for disease terms. There is a single center version that allow identifiers and includes only the phenotype and analysis module (http://phenodb.org) and a tool for larger studies that also includes a sample module and an ELSI module for storage and review of consents (http://researchphenodb.net). Both are freely available for download; http://phenodb.org can be used by individual users to try it out (it is toggled to have only deidentified data). PhenoDB has been in use for the Baylor-Hopkins Center for Mendelian Genomics since March 2012 and holds information on over 5000 individuals from ~3000 families. It has proved efficient and effective in novel disease gene discovery. It can also be used for a laboratory or clinic.
This presentation will cover current status of the SEER program and review opportunities for use of the data in support of cancer research as it currently exists. It will identify and discuss challenges in more comprehensive data collection and how these are being addressed through new initiatives that will enhance the capacity of the registries to support research in a contemporary research setting.
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