I’ve written numerous times about the power of medical data to improve healthcare in the past year or so. A good example of the potential comes via a recently published paper, which describes how a consortium led by Swansea University Medical School aggregated data to forge the largest database of people with epilepsy in the world.
The team wanted to see if specific features of clinical epilepsy aggregate within families, and indeed whether there are distinct family syndromes that could help to better inform genetic research.
The data was derived from over 300 families where at least three members suffered from unprovoked seizures. The analysis revealed that the families would often have a range of epilepsies that were the same by diagnosis. What’s more, some forms that were previously thought of as rare types of epilepsy were actually more common than had been appreciated.
“Epilepsy has a significant effect on people’s lives and this project has increased our knowledge on how some kinds of epilepsy run in families. We are looking forward to further results from the study in the future which will may help us develop new epilepsy treatment plans,” the authors say.
Having access to this kind of data could also inform machine learning based efforts. For instance, a recent project from UCL utilized machine learning to better spot epilepsy in children.
The research, which was a collaborative project between Young Epilepsy, UCL Great Ormond Street Institute of Child Health and the University of Cambridge, focused on Focal Cortical Dysplasia, which is a major cause of epilepsy in children. It describes the way the brain fails to form normally, and because the abnormalities tend to be small, they tend to be very difficult to pick up on MRI scans.
What would make such work even cooler is if they could predict the onset of a fit before it occurs, raising the potential of then intervening to prevent the seizure. That’s exactly what a team from Rice University set out to do. The team developed an algorithm to predict when seizures might occur. After several iterations and a large dose of testing, it was able to predict seizures at least two minutes before their onset.
A team now hope to bring such predictive capabilities to market via a smartphone app that can predict seizures in advance. The project, which was documented in a recent paper, aims to combine information about seizure activity, medication, and other lifestyle factors with things such as environmental data and brain recordings. The app will use this data to predict the likelihood of a seizure occurring that day.
The app aims to provide users with probability ratings across five distinct risk levels (of 20% increments), but the app can become more honed as it tunes itself to your individual behaviors and seizure patterns. The developers hope it will allow users to modify their lifestyle according to the risk of an event occurring.
The app was trained using the world’s longest continuous database of brain recordings. The data was generated through a previous, three year long, trial that looked at developing an implantable seizure warning device. That trial showed the potential to predict seizures, but the results were not accurate all the time.
These are just a few examples of how data can be used to improve the lives of people suffering from epilepsy. It’s a journey that we’ve only just embarked upon, and it will be fascinating to see just where it ends up.