Lund University Team Progress Machine Algorithms that Beat Clinicians in Predicting Alzheimer’s Disease

Lund University Team Progress Machine Algorithms that Beat Clinicians in Predicting Alzheimer’s Disease

A group of Swedish researchers from Lund University has developed algorithms that can identify biomarkers used to better boost the diagnostic accuracy of Alzheimer’s disease. By combining p-tau with other fluid biomarkers and diagnostic data, researchers led by Oskar Hansson and Sebastian Palmqvist report improved ability to actually predict Alzheimer’s disease. In the study, the Swedish researchers reveal an algorithm that can predict Alzheimer’s disease onset within two to six years at a 90% accuracy level. The study, focusing on individuals with a subjective memory complaint, combined plasma p-tau, APOE genotype, executive function, and memory scores into a robust algorithmic function used for the predictions. The study assessed the ability of actual human physicians to predict the outcome, which totaled 72%, considerably lower than the machine.

The Study

The research team first analyzed data among 340 study participants in a study known as the BIOFINDER Cohort. 164 of the individuals were assessed to have subjective cognitive decline and 176 had what seemed like mild cognitive impairment.

Factoring in a number of variables from demographics and cerebrospinal fluid to ...

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