Mining healthcare data to understand traumatic injury

Mining healthcare data to understand traumatic injury

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Medicine has a big data problem. National healthcare registries provide invaluable
data about patients and the reasons why they visit clinics and hospitals. Unfortunately, there’s so much of it, and
relatively little has been done to organize it in a way that’s meaningful for understanding
patient experiences. That makes probing by traditional research
methods virtually impossible. How are researchers addressing this problem? One team has drawn inspiration from genomics. Using an algorithm routinely applied to study
co-expression patterns of multiple genes, they’ve identified patterns in the circumstances
surrounding people experiencing a traumatic brain injury. This approach could help clinicians spot markers
of an impending injury before it happens. The team started with population-wide healthcare
data spanning 9 year for residents of Ontario, Canada. According to that information, nearly a quarter
million patients visited an emergency department or acute care setting for a first-time traumatic
brain injury (or TBI) during that period. Studies have recently established that TBI
can be a result of various pre-existing conditions that can modify the risk of injury. These include depressive and substance-use
disorders, vascular disease, and medication effects—each of which can itself be affected
by factors such as age and socioeconomic status. That makes for a vastly complex web of possible
associations that is humanly impossible to untangle. Computationally, however, the task is feasible. For this, the team used an approach called
multiple testing. As its name suggests, the technique simultaneously
tests thousands of connections between bits of data, and is normally used to identify
significant associations in genetic research. Multiple testing grouped a total of 2600 codes
used by healthcare providers to classify patients’ conditions into categories. Then, using factor analysis, those categories
were whittled down to 43 that were significantly related to TBI versus non-TBI hospital visits. Sorted by effect size, the factors topping
the list included those linked to environmental exposures, assault and child abuse, and the
adverse effects of medications and drugs in the years leading up to TBI. These findings suggest that such factors could
be critical in assessing patients’ susceptibility to brain injury. And they point to the complexity of social
circumstances surrounding an individual. But they’re not necessarily definitive. Further refinement is needed to untangle the
complex interplay of factors to better understand TBI in general. Still, the study has much to offer. The team’s method provides a way of mining
the piles of useful healthcare data to prevent injury and deliver precision medicine.

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