首页|University of Eastern Finland Reports Findings in Machine Learning (Machine lear ning prediction of future amyloid beta positivity in amyloid-negative individual s)
University of Eastern Finland Reports Findings in Machine Learning (Machine lear ning prediction of future amyloid beta positivity in amyloid-negative individual s)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating in Kuopio, Finlan d,by NewsRx journalists,research stated,"The pathophysiology of Alzheimer's d isease (AD) involves -amyloid (A) accumulation. Early identification of individ uals with abnormal-amyloid levels is crucial, but A quantification with positro n emission tomography (PET) and cerebrospinal fluid (CSF) is invasive and expens ive." The news reporters obtained a quote from the research from the University of Eas tern Finland, "We propose a machine learning framework using standard non-invasi ve (MRI, demographics, APOE, neuropsychology) measures to predict future A -posi tivity in A -negative individuals. We separately study A -positivity defined by PET and CSF. Cross-validated AUC for 4-year A conversion prediction was 0.78 for the CSF-based and 0.68 for the PET-based A definitions. Although not trained for the clinical status-change prediction, the CSF-based model excelled in predict ing future mild cognitive impairment (MCI)/dementia conversion in cognitively no rmal/MCI individuals (AUCs, respectively, 0.76 and 0.89 with a separate dataset).Standard measures have potential in detecting future A -positivity and assessi ng conversion risk, even in cognitively normal individuals."
KuopioFinlandEuropeAmyloidCyborg sEmerging TechnologiesMachine LearningPeptides and ProteinsProteins
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