Machine learning helps to assess cognitive brain health

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A team of researchers have employed a novel application of supervised machine learning and predictive modeling to demonstrate and validate a clinical decision support screening tool for Alzheimer’s disease.

While cognitive assessment tools are widely used by clinicians, detection accuracy and reliability remain problems for determining memory function and brain health with these tools.

However, researchers have successfully leveraged MemTrax—a simple online memory test using images recognition—for episodic-memory screening and assessing cognitive impairment.

In a study, published in the Journal of Alzheimer’s Disease, researchers present supervised machine learning as a modern approach and new value-added complementary tool in cognitive brain health assessment and related patient care and management.

“Our novel application of supervised machine learning and predictive modeling helps to demonstrate and validate cross-sectional utility of MemTrax in assessing early-stage cognitive impairment and general screening for AD,” conclude the study’s authors.

“With its widespread prevalence and escalating incidence and public health burden, it is imperative to ensure that the tools clinicians use for testing and managing Alzheimer’s disease and other related cognitive conditions are optimal,” says Stella Batalama, dean of Florida Atlantic University’s College of Engineering and Computer Science. “Results from this important study provide new insights and discovery that has set the stage for future impactful and significant research.”

Also See: Machine learning helps to identify early signs of Alzheimer’s

“Findings from our study provide an important step in advancing the approach for clinically managing a very complex condition like Alzheimer’s disease,” said Michael Bergeron, senior author and senior vice president of development and applications at SIVOTEC Analytics.

“By analyzing a wide array of attributes across multiple domains of the human system and functional behaviors of brain health, informed and strategically directed advanced data mining, supervised machine learning, and robust analytics can be integral, and in fact necessary, for healthcare providers to detect and anticipate further progression in this disease and myriad other aspects of cognitive impairment,” Bergeron adds.

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