A Clinically-Translatable Machine Learning Algorithm for the Prediction of Alzheimer's Disease Conversion in Individuals with Mild and Premild Cognitive Impairment.

@article{Grassi2018ACM,
  title={A Clinically-Translatable Machine Learning Algorithm for the Prediction of Alzheimer's Disease Conversion in Individuals with Mild and Premild Cognitive Impairment.},
  author={Massimiliano Grassi and Giampaolo Perna and Daniela Caldirola and Koen R. J. Schruers and Ranjan Duara and David A. Loewenstein},
  journal={Journal of Alzheimer's disease : JAD},
  year={2018},
  volume={61 4},
  pages={
          1555-1573
        }
}
BACKGROUND Available therapies for Alzheimer's disease (AD) can only alleviate and delay the advance of symptoms, with the greatest impact eventually achieved when provided at an early stage. Thus, early identification of which subjects at high risk, e.g., with MCI, will later develop AD is of key importance. Currently available machine learning algorithms achieve only limited predictive accuracy or they are based on expensive and hard-to-collect information. OBJECTIVE The current study aims… CONTINUE READING
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