In recent years, researchers have sought to improve how we understand the data that providers gather from diagnostic tools like the multiple sleep latency test (MSLT).
copyright by www.sleepreviewmag.com
While the MSLT is the gold standard for diagnosing hypersomnias, including narcolepsy type 1, it doesn’t always detect conditions with arbitrary thresholds that well, such as narcolepsy type 2. This can be a problem, particularly since years of untreated narcolepsy can significantly compromise patient quality of life.
We spoke with Logan Schneider, MD, one of the authors of the study, “ Improved Primary CNS Hypersomnia Diagnosis With Statistical Machine Learning ” on how machine learning can help clinicians and researchers do a better job of differentiating narcolepsy type 1, narcolepsy type 2, and idiopathic hypersomnia. (This Q&A has been lightly edited for clarity.)
Sleep Review (SR): Why did you find it essential to look into whether we could improve the diagnostic accuracy of hypersomnias like narcolepsy type 2 using machine learning?
Schneider: The MSLT is great at detecting type 1 narcolepsy, and it was primarily devised with type 1 narcolepsy patients (with that distinctive phenotype) in mind.
We pulled the data we have of people who test positive in the MSLT, including those people with untreated sleep apnea, shift work disorders, or even sleep deprivation and put that information into a computer algorithm that not only [looked at] MSLT test data, but also the data on what these patients’ sleep looked like the night before. We thought that might give us information about what is defined as abnormal.
What we did here was an exploratory analysis to provide us with information to understand the disorders and differentiate among the primary hypersomnias we examined.
SR: What made you and your colleagues use statistical machine learning
SR: What made you and your colleagues use statistical machine learning as a tool to differentiate these different conditions better?
Schneider: We used multiple machine learning models, using a slightly less biased approach to put in the data we had, to see what new things these models could teach us about these disorders. [Less biased meaning he did not start with a specific hypothesis in mind.] In this study, we used machine learning with a spirit of curiosity. Some of these conditions are hard to diagnose because they’re arbitrarily defined by the criteria we’ve imposed.
We pulled the data we have of people who test positive in the MSLT, including those people with untreated sleep apnea, shift work disorders, or even sleep deprivation and put that information into a computer algorithm that not only [looked at] MSLT test data, but also the data on what these patients’ sleep looked like the night before. We thought that might give us information about what is defined as abnormal.[…]
read more – copyright by www.sleepreviewmag.com
In recent years, researchers have sought to improve how we understand the data that providers gather from diagnostic tools like the multiple sleep latency test (MSLT).
copyright by www.sleepreviewmag.com
We spoke with Logan Schneider, MD, one of the authors of the study, “ Improved Primary CNS Hypersomnia Diagnosis With Statistical Machine Learning ” on how machine learning can help clinicians and researchers do a better job of differentiating narcolepsy type 1, narcolepsy type 2, and idiopathic hypersomnia. (This Q&A has been lightly edited for clarity.)
Sleep Review (SR): Why did you find it essential to look into whether we could improve the diagnostic accuracy of hypersomnias like narcolepsy type 2 using machine learning?
Schneider: The MSLT is great at detecting type 1 narcolepsy, and it was primarily devised with type 1 narcolepsy patients (with that distinctive phenotype) in mind.
We pulled the data we have of people who test positive in the MSLT, including those people with untreated sleep apnea, shift work disorders, or even sleep deprivation and put that information into a computer algorithm that not only [looked at] MSLT test data, but also the data on what these patients’ sleep looked like the night before. We thought that might give us information about what is defined as abnormal.
What we did here was an exploratory analysis to provide us with information to understand the disorders and differentiate among the primary hypersomnias we examined.
SR: What made you and your colleagues use statistical machine learning
SR: What made you and your colleagues use statistical machine learning as a tool to differentiate these different conditions better?
Schneider: We used multiple machine learning models, using a slightly less biased approach to put in the data we had, to see what new things these models could teach us about these disorders. [Less biased meaning he did not start with a specific hypothesis in mind.] In this study, we used machine learning with a spirit of curiosity. Some of these conditions are hard to diagnose because they’re arbitrarily defined by the criteria we’ve imposed.
We pulled the data we have of people who test positive in the MSLT, including those people with untreated sleep apnea, shift work disorders, or even sleep deprivation and put that information into a computer algorithm that not only [looked at] MSLT test data, but also the data on what these patients’ sleep looked like the night before. We thought that might give us information about what is defined as abnormal.[…]
read more – copyright by www.sleepreviewmag.com
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